Improving Classifier Performance Using Feature Selection with Ensemble Learning

Authors(2) :-Bhavesh Patankar, Dr. Vijay Chavda

One of the critical task in data mining is classification. It is very much important in classification to achieve maximum accuracy. In the field of data mining, numerous classifiers are present for the classification task. Each classification techniques have their pros and cons. Some of the techniques work well with certain data sets while other techniques work well with other data sets. There have been many techniques evolved for improving classification accuracy. One of such technique is pre-processing which helps in improving quality of the data. Another method is to combine the classifiers, which will in turn improve the classification accuracy. In this paper, empirical study is been done on various techniques for improving classification accuracy. One of the technique is feature selection, which will select best features from the available features in the data set. Other approach is ensemble learning which combines many classifiers to improve the classification accuracy.

Authors and Affiliations

Bhavesh Patankar
Department of Computer Science, Hemchandracharya North Gujarat University, Patan, Gujarat, India.
Dr. Vijay Chavda

Classification; Pre-processing; Feature Selection; Ensemble Learning;

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Publication Details

Published in : Volume 1 | Issue 1 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 44-48
Manuscript Number : CSEIT16119
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Bhavesh Patankar, Dr. Vijay Chavda, "Improving Classifier Performance Using Feature Selection with Ensemble Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1, Issue 1, pp.44-48, July-August-2016.
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