In this paper, we are interested in determining relevant attributes for multi-class
discrimination of binary data. Given a set of observations described by the presence or …

The analysis of groups of binary data can be achieved by logical based approaches. These
approaches identify subsets of relevant Boolean variables to characterize observations and …

Learning algorithms aim for accuracy of classification but this depends on a choice of
heuristic metric to measure performance and also on the proper consideration and …

The present study proposes an unsupervised method for selection of feature subsets, which
retain sufficient information for classification purposes. Multiple alternative physically …

Feature selection in binary datasets is an important task in many real world machine
learning applications such as document classification, genomic data analysis, and image …

With the advent of big data, interest for new data mining methods has increased
dramatically. The main drawback of traditional data mining methods is the lack of …

For a binary classification problem, the hypothesis testing is studied, that a component of the
observation vector is not effective, ie, that component carries no information for the …

Binary Classification plays an important role in machine learning. For linear classification,
SVM is the optimal binary classification method. For nonlinear classification, the SVM …

This paper presents an overview of several methods that can be used to improve recognition
of a weak class in binary classification problem. We illustrated this problem in the context of …

Multi-class classification problem has become a challenging problem in bioinformatics
research. The problem becomes more difficult as the number of classes increases …