Main Article Content
The success of a machine learning algorithm depends on quality of data .The data given for classification, should not contain
irrelevant or redundant attributes. This increases the processing time. The data set, selected for classification should contain the right attributes
for accurate results. Feature selection is an essential data processing step, prior to applying a learning algorithm. Here we discuss some basic
feature selection models and evaluation function. Experimental results are compared for individual datasets with filter and wrapper model.
Keywords: Data mining , feature selection , filter model, wrapper model, classification
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.