A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.

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An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory

Lecture Notes in Computer Science - Rough Sets, Fuzzy Sets, Data Mining and Granular Computing ◽

10.1007/978-3-642-10646-0_21 ◽

2009 ◽

pp. 176-183 ◽

Cited By ~ 3

Author(s):

Na Jiao ◽

Duoqian Miao

Keyword(s):

Set Theory ◽

Rough Set ◽

Gene Selection ◽

Rough Set Theory ◽

Selection Algorithm ◽

Efficient Gene ◽

Tolerance Rough Set ◽

Gene Selection Algorithm

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Classification Performance Improvement Using Random Subset Feature Selection Algorithm for Data Mining

Big Data Research ◽

10.1016/j.bdr.2018.02.007 ◽

2018 ◽

Vol 12 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

Lakshmipadmaja D ◽

B. Vishnuvardhan

Keyword(s):

Data Mining ◽

Feature Selection ◽

Performance Improvement ◽

Classification Performance ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Random Subset

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An exact feature selection algorithm based on rough set theory

Complexity ◽

10.1002/cplx.21526 ◽

2014 ◽

Vol 20 (5) ◽

pp. 50-62 ◽

Cited By ~ 4

Author(s):

Mohammad Taghi Rezvan ◽

Ali Zeinal Hamadani ◽

Seyed Reza Hejazi

Keyword(s):

Feature Selection ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Selection Algorithm ◽

Feature Selection Algorithm

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Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems

Defence Science Journal ◽

10.14429/dsj.66.10802 ◽

2016 ◽

Vol 66 (6) ◽

pp. 612 ◽

Cited By ~ 14

Author(s):

M.R. Gauthama Raman ◽

K. Kannan ◽

S.K. Pal ◽

V. S. Shankar Sriram

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Rough Set ◽

Network Architecture ◽

Cyber Attacks ◽

Intrusion Detection Systems ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Detection Systems ◽

Helly Property

Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.

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Hybrid Rough Set with Black Hole Optimization Based Feature Selection Algorithm for Protein Structure Prediction

International Journal of Advanced Intelligence Paradigms ◽

10.1504/ijaip.2018.10023064 ◽

2018 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

M. Bagyamathi ◽

Ahmad Taher Azar ◽

H. Hannah Inbarani

Keyword(s):

Black Hole ◽

Feature Selection ◽

Protein Structure ◽

Protein Structure Prediction ◽

Rough Set ◽

Structure Prediction ◽

Selection Algorithm ◽

Feature Selection Algorithm

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A NOVEL FEATURE SELECTION ALGORITHM WITH SUPERVISED MUTUAL INFORMATION FOR CLASSIFICATION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213013500279 ◽

2013 ◽

Vol 22 (04) ◽

pp. 1350027

Author(s):

JAGANATHAN PALANICHAMY ◽

KUPPUCHAMY RAMASAMY

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Mutual Information ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Class A ◽

Selection Algorithms ◽

The Relationship ◽

Class Variable

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms.

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