Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP

Loo, C.K. and Rao, M.V.C. (2005) Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP. IEEE Transactions on Knowledge and Data Engineering, 17 (11). pp. 1589-1593. ISSN 1041-4347

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Official URL: http://dx.doi.org/10.1109/TKDE.2005.173

Abstract

In this paper, an accurate and effective probabilistic plurality voting method to combine outputs from multiple Simplified Fuzzy ARTMAP (SFAM) classifiers is presented. Five ELENA benchmark problems and five medical benchmark data sets have been used to evaluate the applicability and performance of the proposed Probabilistic Ensemble Simplified Fuzzy ARTMAP (PESFAM) network. Among the five benchmark problems in ELENA project, PESFAM outperforms the SFAM and Multi-layer Perceptron (MLP) classifier. In addition, the effectiveness of the proposed PESFAM is delineated in medical diagnosis applications. For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. In addition, the a posteri probability of the predicted class can be used to measure the prediction reliability of PESFAM. The experiments demonstrate the potential of the proposed multiple SFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent medical diagnosis tool.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 14 Sep 2011 06:17
Last Modified: 14 Sep 2011 06:17
URI: http://shdl.mmu.edu.my/id/eprint/2175

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