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Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature(More)
A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient(More)
In machine learning, feature ranking (FR) algorithms are used to rank features by relevance to the class variable. FR algorithms are mostly investigated for the feature selection problem and less studied for the problem of ranking. This paper focuses on the latter. A question asked about the problem of ranking given in the terminology of FR is: as different(More)
Transmembrane protein 16A (TMEM16A), also called Ano1, is a Ca(2+) activated Cl(-) channel expressed widely in mammalian epithelia, as well as in vascular smooth muscle and some tumors and electrically excitable cells. TMEM16A inhibitors have potential utility for treatment of disorders of epithelial fluid and mucus secretion, hypertension, some cancers and(More)
  • Apurva Y. Chaudhari, Satish. S. Banait, +12 authors H. A. Babri
  • 2017
Feature subset selection is a crucial phase in modeling accurate classifiers in data mining and machine learning, especially with High Dimensional Small Sized (HDSS) data. LDA can also be used for feature selection as an efficient measure for evaluation of the feature subset. While LDA is applied to feature selection on HDSS data and class imbalance, it(More)