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Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set

Author(s): C. Lakshmi Devasena

Journal: Bonfring International Journal of Man Machine Interface
ISSN 2250-1061

Volume: 01;
Issue: Inaugural Special Issue;
Start page: 05;
Date: 2011;
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Keywords: IRIS | Fuzzy clustering | DTNB Classifier | RIDOR Classifier | Conjunctive Rule Classifier

In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rule-based classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.
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