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| Author(s): |
Rodrigo Elias Bianchi
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2008-09-26 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
Francisco Javier Ramirez Fernandez;
Zhao Liang;
Pedro Paulo Balbi de Oliveira;
Ivan Nunes da Silva
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| Advisor: | André Carlos Ponce de Leon Ferreira de Carvalho; Maria Cristina Ferreira de Oliveira |
| Abstract | |
Non-symbolic Machine Learning techniques, like Artificial Neural Networks, Support Vector Machines and Ensembles of classifiers have shown a good performance when they are used in data analysis. The strong limitation regarding the use of these techniques is the lack of comprehensibility of the knowledge stored in their internal structure. This Thesis presents an investigation of methods capable of extracting comprehensible representations of the knowledge acquired by these non-symbolic techniques, here named black box, during their learning process. The main contribution of this work is the proposal of a new pedagogical method for rule extraction that explains the classification process followed by non-symbolic techniques. This new method is based on the optimization (maximization) of the similarity between classification rankings produced by symbolic and non-symbolic (from where the internal knowledge is being extracted) Machine Learning techniques. Experiments were performed for several datasets and the results obtained suggest a good potential of the proposed method (AU) | |