Using covariates for improving the minimum redundancy maximum relevance feature selection method

Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets

Discriminative Feature Extraction by a Neural Implementation of Canonical Correlation Analysis

The canonical correlation analysis (CCA) aims at measuring linear relationships between two sets of variables (views) that can be used for feature extraction in classification problems with multiview data. However, the correlated features extracted by the CCA may not be class discriminative, since CCA does not utilize the class labels in its traditional formulation. Although there is a method called discriminative CCA (DCCA) that aims to increase the discriminative ability of CCA inspired from the linear discriminant analysis (LDA), it has been shown that the extracted features with this method are identical to those by the LDA with respect to an orthogonal transformation. Therefore, DCCA is simply equivalent to applying single-view (regular) LDA to each one of the views separately. Besides, DCCA and the other similar DCCA approaches have generalization problems due to the sample covariance matrices used in their computation, which are sensitive to outliers and noisy samples. In this paper, we propose a method, called discriminative alternating regression (D-AR), to explore correlated and also discriminative features. D-AR utilizes two (alternating) multilayer perceptrons, each with a linear hidden layer, learning to predict both the class labels and the outputs of each other. We show that the features found by D-AR on training sets significantly accomplish higher classification accuracies on test sets of facial expression recognition, object recognition, and image retrieval experimental data sets

Online Naive Bayes Classification for Network Intrusion Detection

Intrusion detection system (IDS) is an important component to ensure network security. In this paper we build an online Naive Bayes classifier to discriminate normal and bad (intrusion) connections on KDD 99 dataset for network intrusion detection. The classifier starts with a small number of training examples of normal and bad classes; then, as it classifies the rest of the samples one at a time, it continuously updates the mean and the standard deviations of the features (IDS variables). We present experimental results of parameter updating methods and their parameters for the online Naive Bayes classifier. The obtained results show that our proposed method performs comparably to the simple incremental update

Parkinson Hastalığının Ses Disfonilerinden Teşhisi için bir Ses Veritabanı Oluşturulması ve Örüntülerinin Kullanımı

Bu çalısmanın amacı, deneklerden toplanan ses örneklerinden ölçütler çıkarıp disfoniyi tespit ederek, Parkinson hastalarını sağlıklı deneklerden ayırmaktır. Bu amaçla, çalısma kapsamında 21 tanesi Parkinson hastası olmak üzere toplam 41 kisiden ses kayıtları alınmıstır. Her bir denekten alınan 26 farklı ses örneğinden zaman-frekans tabanlı öznitelikler çıkarılmıstır. Bu öznitelikler ayrı ayrı k-en yakın komsu, çok katmanlı algılayıcı ve destek vektör makineleri sınıflandırıcılarına beslenerek sistemlerin Parkinson Hastalığının teshisindeki doğruluk, duyarlılık ve özgüllükleri ölçülmüstür. Bununla birlikte, her hastanın ses örneklerinden çıkarılan öznitelikler maksimum, minimum, ortalama, medyan ve standart sapma merkezi eğilim ölçüleri ile temsil edilmis ve sınıflandırıcılar bu öznitelikler ile de çalıstırılmıstır. Elde edilen sonuçlar, hastalardan alınan farklı ses örneklerinin merkezi eğilim ölçülerinden medyan ve ortalama ile temsil edilmesinin en istikrarlı ve basarılı sonuçları verdiğini göstermistir