TITLE:
Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach
AUTHORS:
Sharareh R. Niakan Kalhori, Xiao-Jun Zeng
KEYWORDS:
ISULM; Integration Supervised and Unsupervised Learning; Classification; Accuracy; Tuberculosis
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.4,
March
18,
2014
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.