A Hybrid Feature Selection Algorithm

Feature selection algorithm in intrusion detection, data mining and pattern recognition plays a crucial role, it deletes unrelated and redundant features of the original data set to the optimal feature subset which are applied to some evaluation criteria. Due to the low accuracy, the high false posi...

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Bibliographic Details
Published in2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS) pp. 104 - 107
Main Authors Chunyong Yin, Luyu Ma, Lu Feng, Jin Wang, Zhichao Yin, Jeong-Uk Kim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Correlation
Feature extraction
feature selection
Information technology
Intrusion detection
Pattern recognition
Redundancy
Testing
the largest correlation
the minimum redundancy
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Summary:Feature selection algorithm in intrusion detection, data mining and pattern recognition plays a crucial role, it deletes unrelated and redundant features of the original data set to the optimal feature subset which are applied to some evaluation criteria. Due to the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm, in the paper, we put forward a hybrid feature selection algorithm towards efficient intrusion detection, this algorithm chooses the optimal feature subset by combining the correlation algorithm and redundancy algorithm. Experimental results show that the algorithm shows almost and even better than the traditional feature selection algorithm on the different classifiers.
ISBN:9781467375726
1467375721
DOI:10.1109/AITS.2015.35