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On Feature Selection Algorithms and Feature Selection Stability Measures:A Comparative Analysis


Affiliations
1 Dept. of Computer Science, Hindustan College of Arts and Science, Chennai - 603 103, India
2 Dept. of Computer Applications, Madurai Kamaraj University, Madurai – 625 021, India
 

Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from e-commerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.

Keywords

Data Mining, Feature Selection, Feature Selection Algorithms, Selection Stability, Stability Measures.
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  • On Feature Selection Algorithms and Feature Selection Stability Measures:A Comparative Analysis

Abstract Views: 334  |  PDF Views: 162

Authors

P. Mohana Chelvan
Dept. of Computer Science, Hindustan College of Arts and Science, Chennai - 603 103, India
K. Perumal
Dept. of Computer Applications, Madurai Kamaraj University, Madurai – 625 021, India

Abstract


Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from e-commerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.

Keywords


Data Mining, Feature Selection, Feature Selection Algorithms, Selection Stability, Stability Measures.

References