A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

On Rényi information for ergodic diffusion processes

In this paper we derive explicit formulas of the R\'enyi information, Shannon entropy and Song measure for the invariant density of one dimensional ergodic diffusion processes. In particular, the diffusion models considered include the hyperbolic, the generalized inverse Gaussian, the Pearson, the exponential familiy and a new class of skew-t diffusion

New information inequalities on new generalized f-divergence and applications

In this work, we introduce new information inequalities on new generalized f-divergence in terms of well known Chi-square divergence. Further we obtain relations of other standard divergence as an application of new inequalities by using Logarithmic power mean and Identric mean, together with numerical verification by taking two discrete probability distributions: Binomial and Poisson

General Lebesgue integral inequalities of Jensen and Ostrowski type for differentiable functions whose derivatives in absolute value are h-convex and applications

Some inequalities related to Jensen and Ostrowski inequalities for general Lebesgue integral of differentiable functions whose derivatives in absolute value are h-convex are obtained. Applications for f-divergence measure are provided as well