Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach

Sharareh R. Niakan Kalhori, Xiao-Jun Zeng

1Department of Public Health, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 2Social Determinants of Health Research Center, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Department of Machine Learning and Optimization, School of Computer Science, The University of Manchester, Manchester, UK.

DOI: 10.4236/jcc.2014.24027                

Abstract

We have presented an integrated approach based on supervised and unsupervised learning tech- nique to improve the accuracy of six predictive models. They are developed to predict outcome of tuberculosis treatment course and their accuracy needs to be improved as they are not precise as much as necessary. The integrated supervised and unsupervised learning method (ISULM) has been proposed as a new way to improve model accuracy. The dataset of 6450 Iranian TB patients under DOTS therapy was applied to initially select the significant predictors and then develop six predictive models using decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine algorithms. Developed models have integrated with k-mean clustering analysis to calculate more accurate predicted outcome of tuberculosis treatment course. Obtained results, then, have been evaluated to compare prediction accuracy before and after ISULM application. Recall, Precision, F-measure, and ROC area are other criteria used to assess the models validity as well as change percentage to show how different are models before and after ISULM. ISULM led to improve the prediction accuracy for all applied classifiers ranging between 4% and 10%. The most and least improvement for prediction accuracy were shown by logistic regression and support vector machine respectively. Pre-learning by k- mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy in the process of integrating supervised and unsupervised learning.

Keywords

ISULM; Integration Supervised and Unsupervised Learning; Classification; Accuracy; Tuberculosis

Conflicts of Interest

The authors declare no conflicts of interest.

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