Improving Performance of Classifiers using Rotational Feature Selection Scheme
Shib Sankar Bhowmick, Indrajit Saha, Luis Rato, Debotosh Bhattacharjee
Shib Sankar Bhowmick
Available Online July 2013.
- https://doi.org/10.2991/cse.2013.70How to use a DOI?
- Decision Tree; k-NN; Naive Bayesian; Principal Component Analysis; Rotational Feature Selection; Statistical Test.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/cse.2013.70 ID - Bhowmick2013/07 ER -