You are in:Home/Publications/Azar AT, Inbarani HH, Devi KR (2017). Improved dominance rough set-based classification system. Neural Computing and Applications, 28(8): 2231-2246. DOI 10.1007/s00521-016-2177-z. [ISI Indexed: Impact Factor: 4.215].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Azar AT, Inbarani HH, Devi KR (2017). Improved dominance rough set-based classification system. Neural Computing and Applications, 28(8): 2231-2246. DOI 10.1007/s00521-016-2177-z. [ISI Indexed: Impact Factor: 4.215].
Authors: Not Available
Year: 2017
Keywords: Dominance-based rough set, Decision table, Feature selection, Classification
Journal: Neural Computing and Applications
Volume: 28
Issue: 8
Pages: 2231-2246
Publisher: Springer
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

Feature selection and classification is widely used in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such databases, many methods and techniques have been developed. Rough set is a mathematical theory for dealing with uncertainty. In dominance-based rough set extension of rough set, the set of objects partitioned into pre-defined and preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. This paper suggests improved dominance-based rough set for classification of medical data. Dominance-based rough set can handle ordinal attribute. This paper proposed a technique for applying dominance-based rough set for nominal attribute. This proposed work suggests decision table to determine dominance relation, and then improved dominance-based rough set is applied to find lower, upper, boundary approximations in the entire dataset. Then attribute reduction based on proposed technique is applied to find the essential attribute required for classification. This proposed method can accurately classify medical datasets collected from UCI repository Web sites. This proposed method works in seven different datasets: They are heart disease dataset, Pima Indian diabetes dataset, Breast cancer Wisconsin dataset, heart valve dataset, jaundice datasets, dermatology dataset and lung cancer dataset. Comparing the classification accuracy with rule-based classifier (Zero R, decision table), tree-based classifier (J48, Random forest, Random Tree), neural network-based classifier (multilayer perceptron), lazy classifier (IBk, KStar, LWL), Bayesian-based classifier (Naïve Bayes), benchmark algorithm k-nearest–neighbour, and classical rough set approach, improved dominance-based rough set gives higher accuracy.

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