In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of labeled examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. Therefore, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning. Information about the input space can be encoded by regularization. We first design a semi-supervised learning method for text classification that encodes the correlation of words inferred from seemingly irrelevant unlabeled text. We then propose a multi-task learning framework with a matrix-normal penalty, which compactly encodes the covariance structure of the joint input space of multiple tasks. To capture structure information that is more general than covariance and correlation, we study a class of regularization penalties on model compressibility. Then we design the projection penalty, which encodes the structure information from a dimension reduction while controlling the risk of information loss. Information about the output space can be exploited by error correcting output codes. Using the composite likelihood view, we propose an improved pairwise coding for multi-label classification, which encodes pairwise label density (as opposed to label comparisons) and decodes using variational methods. We then investigate problemdependent codes, where the encoding is learned from data instead of being predefined. We first propose a multi-label output code using canonical correlation analysis, where predictability of the code is optimized. We then argue that both discriminability and predictability are critical for output coding, and propose a max-margin formulation that promotes both discriminative and predictable codes. We empirically study our methods in a wide spectrum of applications, including document categorization, landmine detection, face recognition, brain signal classification, handwritten digit recognition, house price forecasting, music emotion prediction, medical decision, email analysis, gene function classification, outdoor scene recognition, and so forth. In all these applications, our proposed methods for encoding input and output information lead to significantly improved prediction performance.

Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment. Pedestrians, animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise. To detect occurrences of the different objects, these systems are traditionally trained to learn a classification model using a set of images that carry labels corresponding to their content. To obtain high performance with a variety of object appearances, the required amount of data is very large. Acquiring unlabeled images is easy, while the manual work of labeling is both time-consuming and costly. Semi-supervised learning refers to methods that utilize both labeled and unlabeled data, a situation that is highly desirable if it can lead to improved accuracy and at the same time alleviate the demand of labeled data. This has been an active area of research in the last few decades, but few studies have investigated the performance of these algorithms in larger systems. In this thesis, we investigate if and how semi-supervised learning can be used in a large-scale pedestrian detection system. With the area of application being automotive safety, where real-time performance is of high importance, the work is focused around boosting classifiers. Results are presented on a few publicly available UCI data sets and on a large data set for pedestrian detection captured in real-life traffic situations. By evaluating the algorithms on the pedestrian data set, we add the complexity of data set size, a large variety of object appearances and high input dimension. It is possible to find situations in low dimensions where an additional set of unlabeled data can be used successfully to improve a classification model, but the results show that it is hard to efficiently utilize semi-supervised learning in large-scale object detection systems. The results are hard to scale to large data sets of higher dimensions as pair-wise computations are of high complexity and proper similarity measures are hard to find.

Le credit scoring est généralement considéré comme une méthode d’évaluation du niveau du risque associé à un dossier de crédit potentiel. Cette méthode implique l'utilisation de différentes techniques statistiques pour aboutir à un modèle de scoring basé sur les caractéristiques du client.Le modèle de scoring estime le risque de crédit en prévoyant la solvabilité du demandeur de crédit. Les institutions financières utilisent ce modèle pour estimer la probabilité de défaut qui va être utilisée pour affecter chaque client à la catégorie qui lui correspond le mieux: bon payeur ou mauvais payeur. Les seules données disponibles pour construire le modèle de scoring sont les dossiers acceptés dont la variable à prédire est connue. Ce modèle ne tient pas compte des demandeurs de crédit rejetés dès le départ ce qui implique qu'on ne pourra pas estimer leurs probabilités de défaut, ce qui engendre un biais de sélection causé par la non-représentativité de l'échantillon. Nous essayons dans ce travail en utilisant l'inférence des refusés de remédier à ce biais, par la réintégration des dossiers refusés dans le processus d'octroi de crédit. Nous utilisons et comparons différentes méthodes de traitement des refusés classiques et semi supervisées, nous adaptons certaines à notre problème et montrons sur un jeu de données réel, en utilisant les courbes ROC confirmé par simulation, que les méthodes semi-supervisé donnent de bons résultats qui sont meilleurs que ceux des méthodes classiques. / Credit scoring is generally considered as a method of evaluation of a risk associated with a potential loan applicant. This method involves the use of different statistical techniques to determine a scoring model. Like any statistical model, scoring model is based on historical data to help predict the creditworthiness of applicants. Financial institutions use this model to assign each applicant to the appropriate category : Good payer or Bad payer. The only data used to build the scoring model are related to the accepted applicants in which the predicted variable is known. The method has the drawback of not estimating the probability of default for refused applicants which means that the results are biased when the model is build on only the accepted data set. We try, in this work using the reject inference, to solve the problem of selection bias, by reintegrate reject applicants in the process of granting credit. We use and compare different methods of reject inference, classical methods and semi supervised methods, we adapt some of them to our problem and show, on a real dataset, using ROC curves, that the semi-supervised methods give good results and are better than classical methods. We confirmed our results by simulation.

La démocratisation de la « vidéosurveillance intelligente » nécessite le développement d’outils automatiques et temps réel d’analyse vidéo. Parmi ceux-ci, la détection de piétons joue un rôle majeur car de nombreux systèmes reposent sur cette technologie. Les approches classiques de détection de piétons utilisent la reconnaissance de formes et l’apprentissage statistique. Elles souffrent donc d’une dégradation des performances quand l’apparence des piétons ou des éléments de la scène est trop différente de celle étudiée lors de l’apprentissage. Pour y remédier, une solution appelée « contextualisation du détecteur » est étudiée lorsque la caméra est fixe. L’idée est d’enrichir le système à l’aide d’informations provenant de la scène afin de l’adapter aux situations qu’il risque de fréquemment rencontrer. Ce travail a été réalisé en deux temps. Tout d’abord, l’architecture d’un détecteur et les différents outils utiles à sa construction sont présentés dans un état de l’art. Puis la problématique de la contextualisation est abordée au travers de diverses expériences validant ou non les pistes d’amélioration envisagées. L’objectif est d’identifier toutes les briques du système pouvant bénéficier de cet apport afin de contextualiser complètement le détecteur. Pour faciliter l’exploitation d’un tel système, la contextualisation a été entièrement automatisée et s’appuie sur des algorithmes d’apprentissage semi-supervisé. Une première phase consiste à collecter le maximum d’informations sur la scène. Différents oracles sont proposés afin d’extraire l’apparence des piétons et des éléments du fond pour former une base d’apprentissage dite contextualisée. La géométrie de la scène, influant sur la taille et l’orientation des piétons, peut ensuite être analysée pour définir des régions, dans lesquelles les piétons, tout comme le fond, restent visuellement proches. Dans la deuxième phase, toutes ces connaissances sont intégrées dans le détecteur. Pour chaque région, un classifieur est construit à l’aide de la base contextualisée et fonctionne indépendamment des autres. Ainsi chaque classifieur est entraîné avec des données ayant la même apparence que les piétons qu’il devra détecter. Cela simplifie le problème de l’apprentissage et augmente significativement les performances du système. / With the rise of videosurveillance systems comes a logical need for automatic and real-time processes to analyze the huge amount of generated data. Among these tools, pedestrian detection algorithms are essential, because in videosurveillance locating people is often the first step leading to more complex behavioral analyses. Classical pedestrian detection approaches are based on machine learning and pattern recognition algorithms. Thus they generally underperform when the pedestrians’ appearance observed by a camera tends to differ too much from the one in the generic training dataset. This thesis studies the concept of the contextualization of such a detector. This consists in introducing scene information into a generic pedestrian detector. The main objective is to adapt it to the most frequent situations and so to improve its overall performances. The key hypothesis made here is that the camera is static, which is common in videosurveillance scenarios.This work is split into two parts. First a state of the art introduces the architecture of a pedestrian detector and the different algorithms involved in its building. Then the problem of the contextualization is tackled and a series of experiments validates or not the explored leads. The goal is to identify every part of the detector which can benefit from the approach in order to fully contextualize it. To make the contextualization process easier, our method is completely automatic and is based on semi-supervised learning methods. First of all, data coming from the scene are gathered. We propose different oracles to detect some pedestrians in order to catch their appearance and to form a contextualized training dataset. Then, we analyze the scene geometry, which influences the size and the orientation of the pedestrians and we divide the scene into different regions. In each region, pedestrians as well as background elements share a similar appearance.In the second step, all this information is used to build the final detector which is composed of several classifiers, one by region. Each classifier independently scans its dedicated piece of image. Thus, it is only trained with a region-specific contextualized dataset, containing less appearance variability than a global one. Consequently, the training stage is easier and the overall detection results on the scene are improved.

Submitted by Lucas Guerreiro null (lucasg@rc.unesp.br) on 2017-10-16T22:03:24Z No. of bitstreams: 1 LucasGuerreiro_dissertacao.pdf: 2072249 bytes, checksum: 03cb08b42175616dd567a364cf201bcd (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-10-18T18:42:00Z (GMT) No. of bitstreams: 1 guerreiro_l_me_sjrp.pdf: 2072249 bytes, checksum: 03cb08b42175616dd567a364cf201bcd (MD5) / Made available in DSpace on 2017-10-18T18:42:00Z (GMT). No. of bitstreams: 1 guerreiro_l_me_sjrp.pdf: 2072249 bytes, checksum: 03cb08b42175616dd567a364cf201bcd (MD5) Previous issue date: 2017-09-01 / O Aprendizado de Máquina é uma área que vem crescendo nos últimos anos e é um dos destaques dentro do campo de Inteligência Artificial. Atualmente, uma das subáreas mais estudadas é o Aprendizado Semi-Supervisionado, principalmente pela sua característica de ter um menor custo na rotulação de dados de exemplo. A categoria de modelos baseados em grafos é a mais ativa nesta subárea, fazendo uso de estruturas de redes complexas. O algoritmo de competição e cooperação entre partículas é uma das técnicas deste domínio. O algoritmo provê acurácia de classificação compatível com a de algoritmos do estado da arte, e oferece um custo computacional inferior à maioria dos métodos da mesma categoria. Neste trabalho é apresentado um estudo sobre Aprendizado Semi-Supervisionado, com ênfase em modelos baseados em grafos e, em particular, no Algoritmo de Competição e Cooperação entre Partículas (PCC). O objetivo deste trabalho é propor um novo algoritmo de competição e cooperação entre partículas baseado neste modelo, com mudanças na caminhada pelo grafo, com informações de dominância sendo mantidas nas arestas ao invés dos nós; as quais possam melhorar a acurácia de classificação ou ainda o tempo de execução em alguns cenários. É proposta também uma metodologia de avaliação da rede obtida com o modelo de competição e cooperação entre partículas, para se identificar a melhor métrica de distância a ser aplicada em cada caso. Nos experimentos apresentados neste trabalho, pode ser visto que o algoritmo proposto teve melhor acurácia do que o PCC em algumas bases de dados, enquanto o método de avaliação de métricas de distância atingiu também bom nível de precisão na maioria dos casos. / Machine Learning is an increasing area over the last few years and it is one of the highlights in Artificial Intelligence area. Nowadays, one of the most studied areas is Semi-supervised learning, mainly due to its characteristic of lower cost in labeling sample data. The most active category in this subarea is that of graph-based models, using complex networks concepts. The Particle Competition and Cooperation in Networks algorithm (PCC) is one of the techniques in this field. The algorithm provides accuracy compatible with state of the art algorithms, and it presents a lower computational cost when compared to most techniques in the same category. In this project, it is presented a research about semi-supervised learning, with focus on graphbased models and, in special, the Particle Competition and Cooperation in Networks algorithm. The objective of this study is to base proposals of new particle competition and cooperation algorithms based on this model, with new dynamics on the graph walking, keeping dominance information on the edges instead of the nodes; which may improve the accuracy classification or yet the runtime in some situations. It is also proposed a method of evaluation of the network built with the Particle Competition and Cooperation model, in order to infer the best distance metric to be used in each case. In the experiments presented in this work, it can be seen that the proposed algorithm presented better accuracy when compared to the PCC for some datasets, while the proposed distance metrics evaluation achieved a high precision level in most cases.

Treinamento semi-supervisionado é uma metodologia de aprendizado de máquina que conjuga características de treinamento supervisionado e não-supervisionado. Ela se baseia no uso de bases semi-rotuladas (bases contendo dados rotulados e não-rotulados) para o treinamento de classificadores. A adição de dados não-rotulados, mais baratos e geralmente disponíveis em maior quantidade do que os dados rotulados, pode aumentar o desempenho e/ou baratear o custo de treinamento desses classificadores (a partir da diminuição da quantidade de dados rotulados necessários). Esta dissertação analisa duas estratégias para se executar treinamento semi-supervisionado, especificamente em Support Vector Machines (SVMs): formas direta e indireta. A estratégia direta é atualmente mais conhecida e estudada, e permite o uso de dados rotulados e não-rotulados, ao mesmo tempo, em tarefas de aprendizagem de classificadores. Entretanto, a inclusão de muitos dados não-rotulados pode tornar o treinamento demasiadamente lento. Já a estratégia indireta é mais recente, sendo capaz de agregar os benefícios do treinamento semi-supervisionado direto com tempos menores para o aprendizado de classificadores. Esta opção utiliza os dados não-rotulados para pré-processar a base de dados previamente à tarefa de aprendizagem do classificador, permitindo, por exemplo, a filtragem de eventuais ruídos e a reescrita da base em espaços de variáveis mais convenientes. Dentro do escopo da forma indireta, está a principal contribuição dessa dissertação: idealização, implementação e análise do algoritmo split learning. Foram obtidos ótimos resultados com esse algoritmo, que se mostrou eficiente em treinar SVMs de melhor desempenho e em períodos menores a partir de bases semi-rotuladas. / Semi-supervised learning is a machine learning methodology that mixes features of supervised and unsupervised learning. It allows the use of partially labeled databases (databases with labeled and unlabeled data) to train classifiers. The addition of unlabeled data, which are cheaper and generally more available than labeled data, can enhance the performance and/or decrease the costs of learning such classifiers (by diminishing the quantity of required labeled data). This work analyzes two strategies to perform semi-supervised learning, specifically with Support Vector Machines (SVMs): direct and indirect concepts. The direct strategy is currently more popular and studied; it allows the use of labeled and unlabeled data, concomitantly, in learning classifiers tasks. However, the addition of many unlabeled data can lead to very long training times. The indirect strategy is more recent; it is able to attain the advantages of the direct semi-supervised learning with shorter training times. This alternative uses the unlabeled data to pre-process the database prior to the learning task; it allows denoising and rewriting the data in better feature espaces. The main contribution of this Master thesis lies within the indirect strategy: conceptualization, experimentation, and analysis of the split learning algorithm, that can be used to perform indirect semi-supervised learning using SVMs. We have obtained promising empirical results with this algorithm, which is efficient to train better performance SVMs in shorter times from partially labeled databases.

A tarefa de classificação em Aprendizado de Máquina consiste da criação de modelos computacionais capazes de identificar automaticamente a classe de objetos pertencentes a um domínio pré-definido a partir de um conjunto de exemplos cuja classe é conhecida. Existem alguns cenários de classificação nos quais cada objeto pode estar associado não somente a uma classe, mas a várias classes ao mesmo tempo. Adicionalmente, nesses cenários denominados multirrótulo, as classes podem ser organizadas em uma taxonomia que representa as relações de generalização e especialização entre as diferentes classes, definindo uma hierarquia de classes, o que torna a tarefa de classificação ainda mais específica, denominada classificação hierárquica. Os métodos utilizados para a construção desses modelos de classificação são complexos e dependem fortemente da disponibilidade de uma quantidade expressiva de exemplos previamente classificados. Entretanto, para muitas aplicações é difícil encontrar um número significativo desses exemplos. Além disso, com poucos exemplos, os algoritmos de aprendizado supervisionado não são capazes de construir modelos de classificação eficazes. Nesses casos, é possível utilizar métodos de aprendizado semissupervisionado, cujo objetivo é aprender as classes do domínio utilizando poucos exemplos conhecidos conjuntamente com um número considerável de exemplos sem a classe especificada. Neste trabalho são propostos, entre outros, métodos que fazem uso do aprendizado semissupervisionado baseado em desacordo coperspectiva, tanto para a tarefa de classificação multirrótulo plana quanto para a tarefa de classificação hierárquica. São propostos, também, outros métodos que utilizam o aprendizado ativo com intuito de melhorar a performance de algoritmos de classificação semissupervisionada. Além disso, são propostos dois métodos para avaliação de algoritmos multirrótulo e hierárquico, os quais definem estratégias para identificação dos multirrótulos majoritários, que são utilizados para calcular os valores baseline das medidas de avaliação. Foi desenvolvido um framework para realizar a avaliação experimental da classificação hierárquica, no qual foram implementados os métodos propostos e um módulo completo para realizar a avaliação experimental de algoritmos hierárquicos. Os métodos propostos foram avaliados e comparados empiricamente, considerando conjuntos de dados de diversos domínios. A partir da análise dos resultados observa-se que os métodos baseados em desacordo não são eficazes para tarefas de classificação complexas como multirrótulo e hierárquica. Também é observado que o problema central de degradação do modelo dos algoritmos semissupervisionados agrava-se nos casos de classificação multirrótulo e hierárquica, pois, nesses casos, há um incremento nos fatores responsáveis pela degradação nos modelos construídos utilizando aprendizado semissupervisionado baseado em desacordo coperspectiva / In machine learning, the task of classification consists on creating computational models that are able to automatically identify the class of objects belonging to a predefined domain from a set of examples whose class is known a priori. There are some classification scenarios in which each object can be associated to more than one class at the same time. Moreover, in such multilabeled scenarios, classes can be organized in a taxonomy that represents the generalization and specialization relationships among the different classes, which defines a class hierarchy, making the classification task, known as hierarchical classification, even more specific. The methods used to build such classification models are complex and highly dependent on the availability of an expressive quantity of previously classified examples. However, for a large number of applications, it is difficult to find a significant number of such examples. Moreover, when few examples are available, supervised learning algorithms are not able to build efficient classification models. In such situations it is possible to use semi-supervised learning, whose aim is to learn the classes of the domain using a few classified examples in conjunction to a considerable number of examples with no specified class. In this work, we propose methods that use the co-perspective disagreement based learning approach for both, the flat multilabel classification and the hierarchical classification tasks, among others. We also propose other methods that use active learning, aiming at improving the performance of semi-supervised learning algorithms. Additionally, two methods for the evaluation of multilabel and hierarchical learning algorithms are proposed. These methods define strategies for the identification of the majority multilabels, which are used to estimate the baseline evaluation measures. A framework for the experimental evaluation of the hierarchical classification was developed. This framework includes the implementations of the proposed methods as well as a complete module for the experimental evaluation of the hierarchical algorithms. The proposed methods were empirically evaluated considering datasets from various domains. From the analysis of the results, it can be observed that the methods based on co-perspective disagreement are not effective for complex classification tasks, such as the multilabel and hierarchical classification. It can also be observed that the main degradation problem of the models of the semi-supervised algorithms worsens for the multilabel and hierarchical classification due to the fact that, for these cases, there is an increase in the causes of the degradation of the models built using semi-supervised learning based on co-perspective disagreement

O aprendizado semi-supervisionado é um paradigma do aprendizado de máquina no qual a hipótese é induzida aproveitando tanto os dados rotulados quantos os dados não rotulados. Este paradigma é particularmente útil quando a quantidade de exemplos rotulados é muito pequena e a rotulação manual dos exemplos é uma tarefa muito custosa. Nesse contexto, foi proposto o algoritmo Cotraining, que é um algoritmo muito utilizado no cenário semi-supervisionado, especialmente quando existe mais de uma visão dos dados. Esta característica do algoritmo Cotraining faz com que a sua aplicabilidade seja restrita a domínios multi-visão, o que diminui muito o potencial do algoritmo para resolver problemas reais. Nesta dissertação, é proposto o algoritmo Co2KNN, que é uma versão mono-visão do algoritmo Cotraining na qual, ao invés de combinar duas visões dos dados, combina duas estratégias diferentes de induzir classificadores utilizando a mesma visão dos dados. Tais estratégias são chamados de k-vizinhos mais próximos (KNN) Local e Global. No KNN Global, a vizinhança utilizada para predizer o rótulo de um exemplo não rotulado é conformada por aqueles exemplos que contém o novo exemplo entre os seus k vizinhos mais próximos. Entretanto, o KNN Local considera a estratégia tradicional do KNN para recuperar a vizinhança de um novo exemplo. A teoria do Aprendizado Semi-supervisionado Baseado em Desacordo foi utilizada para definir a base teórica do algoritmo Co2KNN, pois argumenta que para o sucesso do algoritmo Cotraining, é suficiente que os classificadores mantenham um grau de desacordo que permita o processo de aprendizado conjunto. Para avaliar o desempenho do Co2KNN, foram executados diversos experimentos que sugerem que o algoritmo Co2KNN tem melhor performance que diferentes algoritmos do estado da arte, especificamente, em domínios mono-visão. Adicionalmente, foi proposto um algoritmo otimizado para diminuir a complexidade computacional do KNN Global, permitindo o uso do Co2KNN em problemas reais de classificação / Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. Semi-supervised learning is particularly useful when labeled data is scarce and difficult to obtain. In this context, the Cotraining algorithm was proposed. Cotraining is a widely used semisupervised approach that assumes the availability of two independent views of the data. In most real world scenarios, the multi-view assumption is highly restrictive, impairing its usability for classifification purposes. In this work, we propose the Co2KNN algorithm, which is a one-view Cotraining approach that combines two different k-Nearest Neighbors (KNN) strategies referred to as global and local k-Nearest Neighbors. In the global KNN, the nearest neighbors used to classify a new instance are given by the set of training examples which contains this instance within its k-nearest neighbors. In the local KNN, on the other hand, the neighborhood considered to classify a new instance is the set of training examples computed by the traditional KNN approach. The Co2KNN algorithm is based on the theoretical background given by the Semi-supervised Learning by Disagreement, which claims that the success of the combination of two classifiers in the Cotraining framework is due to the disagreement between the classifiers. We carried out experiments showing that Co2KNN improves significatively the classification accuracy specially when just one view of training data is available. Moreover, we present an optimized algorithm to cope with time complexity of computing the global KNN, allowing Co2KNN to tackle real classification problems

We wish to understand brain; discover its sophisticated ways of calculations to invent improved computational methods. To decipher any complex system, first its components should be understood. Brain comprises neurons. Neurobiologists use morphologic properties like “somatic perimeter”, “axonal length”, and “number of dendrites” to classify neurons. They have discerned two types of neurons: “interneurons” and “pyramidal cells”, and have a consensus about five classes of interneurons: PV, 2/3, Martinotti, Chandelier, and NPY. They still need a more refined classification of interneurons because they suppose its known classes may contain subclasses or new classes may arise. This is a difficult process because of the great number and diversity of interneurons and lack of objective indices to classify them. Machine learning—automatic learning from data—can overcome the mentioned difficulties, but it needs a data set to learn from. To meet this demand neurobiologists compiled a data set from measuring 67 morphologic properties of 220 interneurons of mouse brains; they also labeled some of the samples—i.e. added their opinion about the sample’s classes. This project aimed to use machine learning to determine the true number of classes within the data set, classes of the unlabeled samples, and the accuracy of the available class labels. We used K-means, seeded K-means, and constrained K-means, and clustering validity techniques to achieve our objectives. Our results indicate that: the data set contains seven classes; seeded K-means outperforms K-means and constrained K-means; chandelier and 2/3 are the most consistent classes, whereas PV and Martinotti are the least consistent ones.

This thesis proposes a novel approach to attribute clustering. It exploits the strength of semi-supervised learning to improve the quality of attribute clustering particularly when labeled data is limited. The significance of this work derives in part from the broad, and increasingly important, usage of attribute clustering to address outstanding problems within the machine learning community. This form of clustering has also been shown to have strong practical applications, being usable in heavyweight industrial applications. Although researchers have focused on supervised and unsupervised attribute clustering in recent years, semi-supervised attribute clustering has not received substantial attention. In this research, we propose an innovative two step iterative semi-supervised attribute clustering framework. This new framework, in each iteration, uses the result of attribute clustering to improve a classifier. It then uses the classifier to augment the training data used by attribute clustering in next iteration. This iterative framework outputs an improved classifier and attribute clustering at the same time. It gives more accurate clusters of attributes which better fit the real relations between attributes. In this study we proposed two new usages for attribute clustering to improve classification: solving the automatic view definition problem for multi-view learning and improving missing attribute-value handling at induction and prediction time. The application of these two new usages of attribute clustering in our proposed semi-supervised attribute clustering is evaluated using real world data sets from different domains.