Improving Performance of Classifiers using Rotational Feature Selection Scheme

DOI

10.2991/cse.2013.70How to use a DOI?

Keywords

Decision Tree; k-NN; Naive Bayesian; Principal Component Analysis; Rotational Feature Selection; Statistical Test.

Abstract

The crucial points in machine learning research are that how to develop new classification methods with strong mathematic background and/or to improve the performance of existing methods. Over the past few decades, researches have been working on these issues. Here, we emphasis the second point by improving the performance of well-known supervised classifiers like Naive Bayesian, Decision Tree and k-Nearest Neighbor. For this purpose, recently developed rotational feature selection scheme is used before performing the classification task. It splits the training data set into different number of rotational non-overlapping subsets. Subsequently, principal component analysis is used for each subset and all the principal components are retained to create an informative set that preserve the variability information of the original training data. Thereafter, such informative set is used to train and test the classifiers. Finally, posterior probability is computed to get the classification results. The effectiveness of the rotational feature selection integrated classifiers are demonstrated quantitatively by comparing with aforementioned classifiers for 10 real-life data sets. Finally, statistical test has been conducted to show the superiority of the results.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shib Sankar Bhowmick
AU  - Indrajit Saha
AU  - Luis Rato
AU  - Debotosh Bhattacharjee
PY  - 2013/07
DA  - 2013/07
TI  - Improving Performance of Classifiers using Rotational Feature Selection Scheme
BT  - Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
SP  - 315
EP  - 320
SN  - 1951-6851
UR  - https://doi.org/10.2991/cse.2013.70
DO  - 10.2991/cse.2013.70
ID  - Bhowmick2013/07
ER  -