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Proceedings Paper

Ensemble of classifiers to improve accuracy of the CLIP4 machine-learning algorithm
Author(s): Lukasz Kurgan; Krzysztof J. Cios
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Paper Abstract

Machine learning, one of the data mining and knowledge discovery tools, addresses automated extraction of knowledge from data, expressed in the form of production rules. The paper describes a method for improving accuracy of rules generated by inductive machine learning algorithm by generating the ensemble of classifiers. It generates multiple classifiers using the CLIP4 algorithm and combines them using a voting scheme. The generation of a set of different classifiers is performed by injecting controlled randomness into the learning algorithm, but without modifying the training data set. Our method is based on the characteristic properties of the CLIP4 algorithm. The case study of the SPECT heart image analysis system is used as an example where improving accuracy is very important. Benchmarking results on other well-known machine learning datasets, and comparison with an algorithm that uses boosting technique to improve its accuracy are also presented. The proposed method always improves the accuracy of the results when compared with the accuracy of a single classifier generated by the CLIP4 algorithm, as opposed to using boosting. The obtained results are comparable with other state-of-the-art machine learning algorithms.

Paper Details

Date Published: 6 March 2002
PDF: 10 pages
Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458395
Show Author Affiliations
Lukasz Kurgan, Univ. of Colorado/Denver and Univ. of Colorado/Boulder (United States)
Krzysztof J. Cios, Univ. of Colorado/Denver and Univ. of Colorado/Boulder and Univ. of Colorado Health Scien (United States)


Published in SPIE Proceedings Vol. 4731:
Sensor Fusion: Architectures, Algorithms, and Applications VI
Belur V. Dasarathy, Editor(s)

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