NDLI: An enhanced feature selection method comprising rough set and clustering techniques

Content Provider	IEEE Xplore Digital Library
Author	Murugan, A. Sridevi, T.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Dr. Ambedkar Gov. Arts Coll., Chennai, India (Murugan, A.) \|\| Mother Teresa Women's Univ., Kodaikanal, India (Sridevi, T.)
Abstract	Feature selection or variable reduction is a fundamental problem in data mining, refers to the process of identifying the few most important features for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number of input features thus to identify the key features and eliminating irrelevant features with no predictive information using clustering technique, K-nearest neighbors (KNN) and rough set. This paper deals with two partition based clustering algorithm in data mining namely K-Means and Fuzzy C Means (FCM). These two algorithms are implemented for original data set without considering the class labels and further rough set theory implemented on the partitioned data set to generate feature subset after removing the outlier by using KNN. Wisconsin Breast Cancer datasets derived from UCI machine learning database are used for the purpose of testing the proposed hybrid method. The results show that the hybrid method is able to produce more accurate diagnosis and prognosis results than the full input model with respect to the classification accuracy.
Starting Page	1
Ending Page	4
File Size	205395
Page Count	4
File Format	PDF
ISBN	9781479939749
e-ISBN	9781479939756
DOI	10.1109/ICCIC.2014.7238376
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-18
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis K-means Feature selection Outlier detection Breast cancer Classification algorithms Partitioning algorithms Data mining Fcm Accuracy Clustering algorithms Classification Machine learning
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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