Analysis of Feature Selection Algorithms and a Comparative study on Heterogeneous Classifier for High Dimensional Data survey

Analysis of Feature Selection Algorithms and a Comparative study on Heterogeneous Classifier for High Dimensional Data survey

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-53 Number-2
Year of Publication : 2017
Authors : Kassahun Azezew Ayidagn, prof. Shilpa Gite
DOI :  10.14445/22315381/IJETT-V53P211

Citation 

Kassahun Azezew Ayidagn, prof. Shilpa Gite "Analysis of Feature Selection Algorithms and a Comparative study on Heterogeneous Classifier for High Dimensional Data survey", International Journal of Engineering Trends and Technology (IJETT), V53(2),59-63 November 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
This paper focuses on the analysis of various feature selection algorithms and a comparative study on heterogeneous classifier predictive accuracy problems to work with high dimensional data. Especially we conduct experimental comparisons of IBK (KNN), SVM, NBTree and J48 on KDD Cup99 intrusion detection dataset and one cancer disease diagnosis microarray datasets and analysis their performance with vote generalizations. Based on the fact a large number of features can cause a noise of data and degrades a performance of learning algorithm.To tackle these problems identifying a suitable feature selection method is essential for a given machine learning algorithm tasks. So feature selection plays a great role in intrusion detection, bioinformatics, and medical data analysis. Thus this paper deals the application of best feature selection techniques to improve learning algorithm predictive accuracy in microarray dataset and KDD (Knowledge Discovery and Data Mining Tools Conference) Cup 99 dataset with a respective classification and feature selection algorithms. basically, this approach shows the application of feature selection algorithms when a large number of features represented in a small sample data and small numbers of features represented with a high number of samples by taking the above two different datasets.

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Keywords
High dimensional data. feature selection algorithm. Heterogeneous classifier. Feature selection.