Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report a...

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

Feature selection process is used to reduce the feature vector length and identify thediscriminative features. Many acoustic-phonetic features including Mel-Frequency CepstralCoefficient (MFCC), Energy, Pitch, Zero-crossing, spectrum were tested individually for Arabicmispronunciation detection using three classifiers; Random Forest, Bayesian classifier, BaggedSupport Vector Machine (SVM). The ...

Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is obtained by Pap smear test. Image segmentation performed by multi-thr...

The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance and computational complexities have been studied previously. This paper compares the performance of classical sequential methods, a floating search method, and the “globally optimal” branch and bound algorithm when applie...

In the paper, we present an empirical evaluation of five feature selection methods: ReliefF, random forest feature selector, sequential forward selection, sequential backward selection, and Gini index. Among the evaluated methods, the random forest feature selector has not yet been widely compared to the other methods. In our evaluation, we test how the implemented feature selection can affect ...

In this paper, we propose a model for automatic classification of Animals using different classifiers Nearest Neighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximal region merging segmentation. The Gabor features are extracted from segmented animal images. Discriminative texture features are then selected using the different feature selection algorithm l...

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Flo...

Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...