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A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach | IEEE Journals & Magazine | IEEE Xplore

A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach


Abstract:

Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally de...Show More

Abstract:

Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.
Published in: IEEE Transactions on NanoBioscience ( Volume: 10, Issue: 3, September 2011)
Page(s): 139 - 151
Date of Publication: 20 October 2011

ISSN Information:

PubMed ID: 22020105
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I. Introduction

Rough SETS offer new approaches to machine learning, knowledge discovery in data, and knowledge support systems. Rough set theory has thus become an important basis for reasoning, inductive learning, and knowledge reduction. The rough set method (the rough set theory-based method) is a new discovery method for a data classification system that is used when there are different types of object data (any applicable knowledge-based data) whose attributes can be reduced and classified in order to provide comprehensive information.

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