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

Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy

The article focuses on the analysis of motivation principles in human resource management and their further verification by factor analysis. The objective is to identify the main motivation principles and their impacts on employee turnover as well as formulate suggested practices to eliminate the negative impact of employee disaffection and turnover. The identification of motivation principles is based on a content analysis of professional and scientific publications aimed at motivation. The results and conclusions of this study were consequently verified by a quantitative survey, the data of which were statistically processed. As a suitable statistical analysis to assess the data from the survey, a factor analysis was chosen. The data for the factor analysis were collected and analysed based on two quantitative surveys focused on the causes of employee turnover. The results of both analyses proved and verified identical principles of employee management that affect job satisfaction and the decisions of employees to stay or leave their current job positions

Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications

A gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection

Feature design is an important part to identify objects of interest into a known number of categories or classes in object detection. Based on the depth-first search for higher order feature functions, the technique of automated feature synthesis is generally considered to be a process of creating more effective features from raw feature data during the run of the algorithms. This dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This thesis presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically in order to solve the given tasks of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation that provides protection against inter-cluster data separation and thus exhibits improved convergence. Additionally, with the GP-EM method, an innovative technique, called the histogram region of interest by thresholds (HROIBT), is introduced for diagnosing protein conformation defects (PCD) from microscopic imagery. The experimental results show that the proposed approach improves the detection accuracy and efficiency of pattern object discovery, as compared to single GP-based feature synthesis methods and also a number of other object detection systems. The GP-EM method projects the hyperspace of the raw data onto lower-dimensional spaces efficiently, resulting in faster computational classification processes