Binary and multicategory classification accuracy of the LSA machine

Authors:
Georgios Lappas;Vivian Ambrosiadou
Affiliations:
University of Hertfordshire, Computer Science Dept., Hatfield, Herts, UK and (TEI) of Western Macedonia, Dept. of Public Relations and Communication Kastoria Campus, Kastoria, Greece;Medical Informatics Laboratory, Aristotle University of Thessaloniki, Thessaloniki, Greece
Venue:
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Year:
2003

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Abstract

The LSA machine is an effective method for predicting a class from linear separable data. LSA machine is based on the combination of Logarithmic Simulated Annealing with the Perceptron Algorithm. In this paper we present and compare the classification accuracy of the LSA machine on two medical databases a) the Winsconsin Breast Cancer Database, which is a binary database with two associated classes and b) the Diabetic Patient Management Database, which is a multicategory database with four associated classes. Many researchers use the Winsconsin Breast Cancer Database (WBCD) database, as a benchmark database for testing their systems. The WBCD database consists of 699 samples with 9 input attributes, The LSA machine is trained on 50% and 75% of the entire dataset and in both cases we obtain a classification accuracy of 98.8% on the remaining samples. This classification accuracy on the test set of samples, to our best of knowledge is the highest reported in the literature. The Diabetic Patient Management database consists of 746 samples with 18 input values and an associated class label denoting one of the four treatments for the patient. The LSA machine for comparison reasons is trained on the 646 samples of the database, obtaining stable classification accuracy over 74% for all four classes, with highest classification accuracy of 87%.