Os recentes avanços da ciência e tecnologia viabilizaram o crescimento de dados em quantidade e disponibilidade. Junto com essa explosão de informações geradas, surge a necessidade de analisar dados para descobrir conhecimento novo e útil. Desse modo, áreas que visam extrair conhecimento e informações úteis de grandes conjuntos de dados se tornaram grandes oportunidades para o avanço de pesquisas, tal como o Aprendizado de Máquina (AM) e a Mineração de Dados (MD). Porém, existem algumas limitações que podem prejudicar a acurácia de alguns algoritmos tradicionais dessas áreas, por exemplo o desbalanceamento das amostras das classes de um conjunto de dados. Para mitigar tal problema, algumas alternativas têm sido alvos de pesquisas nos últimos anos, tal como o desenvolvimento de técnicas para o balanceamento artificial de dados, a modificação dos algoritmos e propostas de abordagens para dados desbalanceados. Uma área pouco explorada sob a visão do desbalanceamento de dados são os problemas de classificação hierárquica, em que as classes são organizadas em hierarquias, normalmente na forma de árvore ou DAG (Direct Acyclic Graph). O objetivo deste trabalho foi investigar as limitações e maneiras de minimizar os efeitos de dados desbalanceados em problemas de classificação hierárquica. Os experimentos realizados mostram que é necessário levar em consideração as características das classes hierárquicas para a aplicação (ou não) de técnicas para tratar problemas dados desbalanceados em classificação hierárquica. / Recent advances in science and technology have made possible the data growth in quantity and availability. Along with this explosion of generated information, there is a need to analyze data to discover new and useful knowledge. Thus, areas for extracting knowledge and useful information in large datasets have become great opportunities for the advancement of research, such as Machine Learning (ML) and Data Mining (DM). However, there are some limitations that may reduce the accuracy of some traditional algorithms of these areas, for example the imbalance of classes samples in a dataset. To mitigate this drawback, some solutions have been the target of research in recent years, such as the development of techniques for artificial balancing data, algorithm modification and new approaches for imbalanced data. An area little explored in the data imbalance vision are the problems of hierarchical classification, in which the classes are organized into hierarchies, commonly in the form of tree or DAG (Direct Acyclic Graph). The goal of this work aims at investigating the limitations and approaches to minimize the effects of imbalanced data with hierarchical classification problems. The experimental results show the need to take into account the features of hierarchical classes when deciding the application of techniques for imbalanced data in hierarchical classification.

Os recentes avanços da ciência e tecnologia viabilizaram o crescimento de dados em quantidade e disponibilidade. Junto com essa explosão de informações geradas, surge a necessidade de analisar dados para descobrir conhecimento novo e útil. Desse modo, áreas que visam extrair conhecimento e informações úteis de grandes conjuntos de dados se tornaram grandes oportunidades para o avanço de pesquisas, tal como o Aprendizado de Máquina (AM) e a Mineração de Dados (MD). Porém, existem algumas limitações que podem prejudicar a acurácia de alguns algoritmos tradicionais dessas áreas, por exemplo o desbalanceamento das amostras das classes de um conjunto de dados. Para mitigar tal problema, algumas alternativas têm sido alvos de pesquisas nos últimos anos, tal como o desenvolvimento de técnicas para o balanceamento artificial de dados, a modificação dos algoritmos e propostas de abordagens para dados desbalanceados. Uma área pouco explorada sob a visão do desbalanceamento de dados são os problemas de classificação hierárquica, em que as classes são organizadas em hierarquias, normalmente na forma de árvore ou DAG (Direct Acyclic Graph). O objetivo deste trabalho foi investigar as limitações e maneiras de minimizar os efeitos de dados desbalanceados em problemas de classificação hierárquica. Os experimentos realizados mostram que é necessário levar em consideração as características das classes hierárquicas para a aplicação (ou não) de técnicas para tratar problemas dados desbalanceados em classificação hierárquica. / Recent advances in science and technology have made possible the data growth in quantity and availability. Along with this explosion of generated information, there is a need to analyze data to discover new and useful knowledge. Thus, areas for extracting knowledge and useful information in large datasets have become great opportunities for the advancement of research, such as Machine Learning (ML) and Data Mining (DM). However, there are some limitations that may reduce the accuracy of some traditional algorithms of these areas, for example the imbalance of classes samples in a dataset. To mitigate this drawback, some solutions have been the target of research in recent years, such as the development of techniques for artificial balancing data, algorithm modification and new approaches for imbalanced data. An area little explored in the data imbalance vision are the problems of hierarchical classification, in which the classes are organized into hierarchies, commonly in the form of tree or DAG (Direct Acyclic Graph). The goal of this work aims at investigating the limitations and approaches to minimize the effects of imbalanced data with hierarchical classification problems. The experimental results show the need to take into account the features of hierarchical classes when deciding the application of techniques for imbalanced data in hierarchical classification.

Data imbalance has been a challenge in many areas of automatic classification. Many popular approaches including over-sampling, under-sampling, and Synthetic Minority Oversampling Technique (SMOTE) have been developed and tested in previous research. A big problem with these techniques is that they try to solve the problem by modifying the original data rather than truly overcome the imbalance and let the classifiers learn. For tasks in areas like remote sensing and depression detection, the imbalanced data challenge also exists. Researchers have made efforts to overcome the challenge by adopting methods at the data pre-processing step. However, in remote sensing and depression detection tasks, the main interest is still on applying different new classifiers such as deep learning which has powerful classification ability but still do not consider data imbalance as prime factor of lower classification performance. In this thesis, we demonstrate the performance of K-CR in our evaluation experiments on a urban land cover classification dataset and on two depression detection datasets. The latter two datasets consist in social media texts (tweets), therefore we propose to adopt a feature selection technique Term Frequency - Category-Based Term Weights (TF-CBTW) and various word embedding techniques (Word2Vec, FastText, GloVe, and language model BERT). This feature selection method was not applied before in similar settings and we show that it helps to improve the efficiency and the results of the K-CR classifier. Our three experiments show that K-CR can achieve comparable performance on the majority classes and better performance on minority classes when compared to other classifiers such as Random Forest, K-Nearest Neighbour, Support Vector Machines, Multi-layer Perception, Convolutional Neural Networks, and Long Short-Term Memory.

<p>Every 12 minutes, 12 women are diagnosed with breast cancer in the US, and 1 dies out of  it. Globally, every 46 seconds, a woman loses her life due to breast cancer, meaning more than  1,800 deaths every day. The condition makes the prediction of breast cancer very important. To  achieve the goal, supervised machine learning (ML) methods are used for breast cancer  likelihood predictions. However, due to imbalance in the real-world data with very low portion  of positive cases, the prediction accuracy of ML models for positive cancer cases was limited. Two procedures were done to address the issues in the study. Firstly, four supervised ML  models, including Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), using WEKA, the industry-standard software, were  applied to the Breast Cancer Surveillance Consortium (BCSC) dataset to assess the impact of the  data imbalance on breast cancer prediction. Secondly, the data was manually built as balanced  (24,558 cases, 12,279 for each class-positive and negative) and unbalanced (99,000 cases for  negative) training datasets and a non-overlapping testing dataset (11,000 cases) based on the  same dataset and a decision support system was developed for two ML models, NB and LR to  tackle the class imbalance issue for breast cancer prediction. Overall, the results indicate that  MLP had the best performance on positive breast cancer prediction with 0.959 sensitivity and  0.907 PPV and balanced dataset predicted better results for all ML models than unbalanced  dataset. Furthermore, the proposed method improved the sensitivity of positive cancer case  prediction from 0.687 to 0.936 using the NB model and from 0.358 to 0.8306 using the LR  model. The improvement demonstrated that the approach provided higher confidence ML-based  predictions and filtered weaker ones, and the technique could efficiently address the class  imbalance issue in breast cancer likelihood prediction and be used in clinical practice.</p>

This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with Uni and Multi Variate Bayesian Model with Shrinkage Priors (BMSP). The problem addressed is the need for accurate and advanced fraud detection techniques due to the increasing sophistication of fraudulent activities. The methodology involves the implementation of GANs and the application of BMSP for variable selection to generate synthetic fraud samples for fraud detection using the augmented dataset. Experimental results demonstrate the effectiveness of the BMSP GAN approach in detecting fraud with improved performance compared to other methods. The conclusions drawn highlight the potential of GANs and BMSP for enhancing fraud detection capabilities and suggest future research directions for further improvements in the field.

Chronic Kidney Disease (CKD) is a common and dangerous health condition that requires early detection and treatment to be effective. Current diagnostic methods are time-consuming and expensive. In this research, we hope to construct a predictive model for CKD utilizing a combination of time series and static variables for early detection of CKD. In this study, we investigate the influence of multimodal approach by combining the predictions from multiple models that utilize different modalities. The ROCKET method is utilized for classification using time series features, whilst the Random Forest approach is employed for static data. XGBoost has been utilized to gain information about feature importance among labs and demographics-comorbidities data. In this study, we use the MIMIC-III database, adopting various strategies to handle data and class imbalance, such as stratification, balancing techniques, and backwards and forward fill for missing value imputation. The evaluation metrics for CKD and non-CKD class labels include precision, recall, F1, and accuracy. Our findings show that aggregating time series data produce contrasting results for labs compared to vitals data. We also addressed the significance of the different demographic, comorbidities and lab events features. The findings indicate that a multimodal approach did not show significant advantages over individual models when the individual models performed suboptimal. The study also found that Ethnicity is more significant than age and gender in predicting CKD. Furthermore, the study revealed some significant features from lab events and comorbidities. The study also provides some recommendations for future work to explore the potential of a multimodal approach further.

This project mainly focuses on the various methods of solving data imbalance problems in the Natural Language Processing (NLP) field. Unbalanced text data is a common problem in many tasks especially the classification task, which leads to the model not being able to predict the minority class well. Sometimes, even we change to some more excellent and complicated model could not improve the performance, while some simple data strategies that focus on solving data imbalanced problems such as over-sampling or down-sampling produce positive effects on the result. The common data strategies include some re-sampling methods that duplicate new data from the original data or remove some original data to have the balance. Except for that, some other methods such as word replacement, word swap, and word deletion are used in previous work as well. At the same time, some deep learning models like BERT, GPT and fastText model, which have a strong ability for a general understanding of natural language, so we choose some of them to solve the data imbalance problem. However, there is no systematic comparison in practicing these methods. For example, over-sampling and down-sampling are fast and easy to use in previous small scales of datasets. With the increase of the dataset, the newly generated data by some deep network models is more compatible with the original data. Therefore, our work focus on how is the performance of various data augmentation techniques when they are used to solve data imbalance problems, given the dataset and task? After the experiment, Both qualitative and quantitative experimental results demonstrate that different methods have their advantages for various datasets. In general, data augmentation could improve the performance of classification models. For specific, BERT especially our fine-tuned BERT has an excellent ability in most using scenarios(different scales and types of the dataset). Still, other techniques such as Back-translation has a better performance in long text data, even it costs more time and has a complicated model. In conclusion, suitable choices for data augmentation methods could help to solve data imbalance problems. / Detta projekt fokuserar huvudsakligen på de olika metoderna för att lösa dataobalansproblem i fältet Natural Language Processing (NLP). Obalanserad textdata är ett vanligt problem i många uppgifter, särskilt klassificeringsuppgiften, vilket leder till att modellen inte kan förutsäga minoriteten Ibland kan vi till och med byta till en mer utmärkt och komplicerad modell inte förbättra prestandan, medan några enkla datastrategier som fokuserar på att lösa data obalanserade problem som överprov eller nedprovning ger positiva effekter på resultatet. vanliga datastrategier inkluderar några omprovningsmetoder som duplicerar nya data från originaldata eller tar bort originaldata för att få balans. Förutom det används vissa andra metoder som ordbyte, ordbyte och radering av ord i tidigare arbete Samtidigt har vissa djupinlärningsmodeller som BERT, GPT och fastText-modellen, som har en stark förmåga till en allmän förståelse av naturliga språk, så vi väljer några av dem för att lösa problemet med obalans i data. Det finns dock ingen systematisk jämförelse när man praktiserar dessa metoder. Exempelvis är överprovtagning och nedprovtagning snabba och enkla att använda i tidigare små skalor av datamängder. Med ökningen av datauppsättningen är de nya genererade data från vissa djupa nätverksmodeller mer kompatibla med originaldata. Därför fokuserar vårt arbete på hur prestandan för olika dataförstärkningstekniker används när de används för att lösa dataobalansproblem, givet datamängden och uppgiften? Efter experimentet visar både kvalitativa och kvantitativa experimentella resultat att olika metoder har sina fördelar för olika datamängder. I allmänhet kan dataförstärkning förbättra prestandan hos klassificeringsmodeller. För specifika, BERT speciellt vår finjusterade BERT har en utmärkt förmåga i de flesta med hjälp av scenarier (olika skalor och typer av datamängden). Ändå har andra tekniker som Back-translation bättre prestanda i lång textdata, till och med det kostar mer tid och har en komplicerad modell. Sammanfattningsvis lämpliga val för metoder för dataökning kan hjälpa till att lösa problem med obalans i data.