Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. Previous research has shown that nearest-neighbor imputation methods can accurately estimate forest volume across a landscape by relating variables of interest to ground data, satellite imagery, and light detection and ranging (LiDAR) data. Alternatively, parametric models, such as linear and non-linear regression and geographic weighted regression (GWR), have been used to estimate net primary production and tree diameter. The goal of this study was to compare various imputation methods to predict forest biomass, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. In this study I compared the predictive performance of, 1) linear regression, GWR, gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest and 2) MSN and k-nn when imputing forest biomass at spatial scales ranging from 5,000 to 50,000 acres. To test the imputation methods a combination of ground inventory plots, LiDAR data, satellite imagery, and climate data were analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k=5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k=3), followed by the GWR model, and the random forest imputation. The GNN method was the least accurate. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k=5), followed by k-nn (k=3), and the random forest method. The GNN method, again, was the least accurate. The accuracy of MSN, the current imputation method used by the Malheur Nation Forest, and k-nn (k=5), the most accurate imputation method from the second chapter, were then compared over 6 spatial scales: 5,000, 10,000, 20,000, 30,000, 40,000, and 50,000 acres. The root mean square difference (RMSD) and bias were calculated for each of the spatial scale samples to determine which was more accurate. MSN was found to be more accurate at the 5,000, 10,000, 20,000, 30,000, and 40,000 acre scales. K-nn (k=5) was determined to be more accurate at the 50,000 acre scale. / Graduation date: 2013

Local feature detection and its applications in different problems are quite popular in vision research. In order to analyze a scene, its invariant features, which are distinguishable in many views of this scene, are used in pose estimation, object detection and augmented reality. However, required performance metrics might change according to the application type / in general, the main metrics are accepted as accuracy and computational complexity. The contributions in this thesis provide improving these metrics and can be divided into three parts, as local feature detection, local feature description and description matching in different views of the same scene. In this thesis an efficient feature detection algorithm with sufficient repeatability performance is proposed. This detection method is convenient for real-time applications. For local description, a novel local binary pattern outperforming state-of-the-art binary pattern is proposed. As a final task, a fuzzy decision tree method is presented for approximate nearest neighbor search. In all parts of the system, computational efficiency is considered and the algorithms are designed according to limited processing time. Finally, an overall system capable of matching different views of the same scene has been proposed and executed in a mobile platform. The results are quite promising such that the presented system can be used in real-time applications, such as augmented reality, object retrieval, object tracking and pose estimation.

Among the various remote sensing methods that can be used to map forest areas, the K Nearest Neighbor (KNN) supervised classification method is becoming increasingly popular for creating forest inventories in some countries. In this study, the utility of the KNN algorithm is evaluated for forest/non-forest/water stratification. Antalya is selected as the study area. The data used are composed of Landsat TM and Landsat ETM satellite images, acquired in 1987 and 2002, respectively, SRTM 90 meters digital elevation model (DEM) and land use data from the year 2003. The accuracies of different modifications of the KNN algorithm are evaluated using Leave One Out, which is a special case of K-fold cross-validation, and traditional accuracy assessment using error matrices. The best parameters are found to be Euclidean distance metric, inverse distance weighting, and k equal to 14, while using bands 4, 3 and 2. With these parameters, the cross-validation error is 0.009174, and the overall accuracy is around 86%. The results are compared with those from the Maximum Likelihood algorithm. KNN results are found to be accurate enough for practical applicability of this method for mapping forest areas.

Many interesting applications involve decision prediction based on a time-series sequence or a set of time-series sequences, which are referred to as time-series classification problems. Past classification analysis research predominately focused on constructing a classification model from training instances whose attributes are atomic and independent. Direct application of traditional classification analysis techniques to time-series classification problems requires the transformation of time-series data into non-time-series data attributes by applying some statistical operations (e.g., average, sum, etc). However, such statistical transformation often results in information loss. In this thesis, we proposed the Time-Series Classification (TSC) technique, based on the nearest neighbor classification approach. The result of empirical evaluation showed that the proposed time-series classification technique had better performance than the statistical-transformation-based approach.

Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors. In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors. In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods. Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting. Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances. Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation. / text

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Age related macular degeneration (AMD) is a public health concern in an aging society. The retinal pigment epithelium (RPE) layer of the eye is a principal site of pathogenesis for AMD. Morphological characteristics of the cells in the RPE layer can be used to discriminate age and disease status of individuals. In this thesis three genotypes of mice of various ages are used to study the predictive abilities of these characteristics. The disease state is represented by two mutant genotypes and the healthy state by the wild-type. Classification analysis is applied to the RPE morphology from the different spatial regions of the RPE layer. Variable reduction is accomplished by principal component analysis (PCA) and classification analysis by the k-nearest neighbor (k-NN) algorithm. In this way the differential ability of the spatial regions to predict age and disease status by cellular variables is explored.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.