Artificial Intelligence (AI) methods are used to build classifiers that give different levels of accuracy and solution explication. The intent of this paper is to provide a way of building a hierarchical classifier composed of several artificial neural networks (ANN’s) organised in a tree-like fashion. Such method of construction allows for partition of the original problem into several sub-problems which can be solved with simpler ANN’s, and be built quicker than a single ANN. As the sub-problems extracted start to be independent of one another, this paves a way to realise the solutions for the individual sub-problems in a parallel fashion. It is observed that incorrect classifications are not random and can be therefore used to find clusters defining sub-problems.
KeywordsConfusion Matrix Electric Power Consumption Satisfying Accuracy Committee Machine Practical Machine Learning Tool
Unable to display preview. Download preview PDF.
- 1.Merz, C.J., Blake, C.L.: Uci repository of machine learning databasesGoogle Scholar
- 2.Smyth, P., Hand, D., Mannila, H.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
- 5.Frank, E., Witten, I.H.: Data mining: practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar
- 6.Sokolic, M., Zwitter, M.: Primary tumor data setGoogle Scholar
- 8.Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
- 9.Leow, W.K., Setiono, R.: FERNN: An algorithm for fast extraction of rules from neural networks. Applied Intelligence 12(1-2), 15–25 (2000)Google Scholar