Rough Classifiers

Lenarcik, Andrzej; Piasta, Zdzisław

doi:10.1007/978-1-4471-3238-7_36

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Rough classifier is an extension of logic and discrete mathematics from rough set theory [12]. Like decision tree, rough classifier is a nonparametric model which suits for the exploratory knowledge discovery and without intervention from users.…”

Section: Preliminariesmentioning

confidence: 99%

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Agent based data classification approach for data mining

Bakar

Othman

Hamdan

et al. 2008

2008 International Symposium on Information Technology

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Classification is one of the tasks in data mining. The form of classifier depends on the classification technique used. For example, neural network produce a set of weight as a classifier, regression form an equation as a predictor while decision tree, C4.5, CART, Rough Set and Bayesian theory generate set of rules known as rule based classifier. Rules are more interpretable by human when compared to other form of classifiers. The process of classification involves applying the rules onto a set of unseen data. There are many issues appeared in rule application process such as more than one rule match, multiple scanning of large rule base and uncertainty. In this study an agent based approach is proposed to improve the rule application process. The proposed agents are embedded within the standard rule application techniques. The result shows the significant improvements in classification time and the number of matched rules with comparable classification accuracy.

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Section: Preliminariesmentioning

confidence: 99%

“…The strength of each rule can be evaluated by the estimation of attribute and decision probabilities. The common measures used in rule base classifiers are rule accuracy, support, coverage, length and the size of the rules [12].…”

Section: Preliminariesmentioning

confidence: 99%

Agent based data classification approach for data mining

Bakar

Othman

Hamdan

et al. 2008

2008 International Symposium on Information Technology

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show abstract

“…By factoring agents, relationships, and behaviors into separate components, more modular and expressive models can be created. Research shows the knowledge discovery is using multi-agent approach for quicker and reliable information retrieval [2][3][4][5][6][7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

“…A group of agents can collectively and collaboratively form a Multi Agent System (MAS) to perform complex and lengthy tasks [1][2][3][4][5][6][7].…”

Section: B Masmentioning

confidence: 99%

“…They are more an instrument for scientific research rather than a tool for generic practical data analysis. Rough classifier is an extension of logic and discrete mathematics from rough set theory [7]. Like decision tree, rough classifier is a nonparametric model which www.ijacsa.thesai.org suits for the exploratory knowledge discovery and without intervention from users.…”

Section: Why Zero-r?mentioning

confidence: 99%

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Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm

Inamdar¹,

Narangale²,

Shinde³

2011

IJACSA

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Abstract-Data mining and multiagent approach has been used successfully in the development of large complex systems. Agents are used to perform some action or activity on behalf of a user of a computer system. The study proposes an agent based algorithm PrePZero-r using Zero-R algorithm in Weka. Algorithms are powerful technique for solution of various combinatorial or optimization problems. Zero-R is a simple and trivial classifier, but it gives a lower bound on the performance of a given dataset which should be significantly improved by more complex classifiers. The Proposed Algorithm called PrePZero-r has significantly reduced time taken to build the model than Zero-R algorithm by removing the Lower Bound Values 0 while preprocessing and comparing the result with class values. Also proposed study introduced new factor "Accuracy (1-e)" for each individual attribute.

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Rough-set inspired approach to knowledge discovery in business databases

Kowalczyk

Piasta

1998

Research and Development in Knowledge Discovery and Data Mining

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Rough Classifiers

Cited by 8 publications

References 4 publications

Agent based data classification approach for data mining

Agent based data classification approach for data mining

Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm

Rough-set inspired approach to knowledge discovery in business databases

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