Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (P...

Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle a...

With the advancement of metagenome data mining science has become focused on microarrays. Microarrays are datasets with a large number of genes that are usually irrelevant to the output class; hence, the process of gene selection or feature selection is essential. So, it follows that you can remove redundant genes and increase the speed and accuracy of classification. After applying the gene se...

Irrelevant, noisy and high dimensional data, containing large number of features, degrades the performance of data mining and machine learning tasks. One of the methods used in data mining to reduce the dimensionality of data is feature selection. Feature selection methods select a subset of features that represents original features in problem domain with high accuracy. Various methods have be...

We show the use of a genetic algorithm for feature subset selection over feature vectors that describe the system calls executed by privileged processes. Genetic feature subset selection significantly reduces the number of features used without adversely affecting the accuracy of the predictions.

Feature subset selection is an essential preprocessing task in data mining. This paper presents a new method called Extended Fuzzy Relative Information Measure for Boundary Samples (EFRIMBS) for dealing with supervised feature subset selection. The proposed algorithm uses boundary samples instead of full set of samples. First, Discretization algorithms such as K-Means, Fuzzy C Means and Median ...

Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is pr...

One of the major challenges when working with software metrics datasets is that some metrics may be redundant or irrelevant to software defect prediction. This may be addressed using feature (metric) selection, which chooses an appropriate subset of features for use in downstream computation. There are three major forms of feature selection: filter-based feature rankers, which uses statistical ...

In machine learning, feature selection is preprocessing step and can be effectively reduce high dimensional data, remove irrelevant data, increase learning accuracy, and improve result comprehensibility. High dimensionality of data take over efficiency and effectiveness points of view in feature selection algorithm. Efficiency stands required time to find a subset of features, and the effective...