MOKBL+MOMs: An interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data
| Autor: | Fatemeh Aghaeipoor, Mohammad Masoud Javidi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Fuzzy rule business.industry Computer science 05 social sciences 050301 education Feature selection 02 engineering and technology Fuzzy control system computer.software_genre Computer Science Applications Theoretical Computer Science Knowledge base Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Pruning (decision trees) business 0503 education computer Software Statistical hypothesis testing Interpretability |
| Zdroj: | Information Sciences. 496:1-24 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2019.04.035 |
| Popis: | This work presents a multi-objective evolutionary linguistic fuzzy system that addresses regression problems, especially those that are dimensional and scalable. A multi-objective knowledge base learning (MOKBL) is developed in the first stage of this model. MOKBL learns the most relevant and least redundant features by considering the desirability of the components of the fuzzy system. At the same time as feature selection, MOKBL slightly tunes the membership functions to provide greater initial adaptation of the fuzzy rule-based system components. In the second stage, multi-objective modifications (MOMs) are organized to modify the generated fuzzy system and to perform post-processing tasks. MOMs more finely tune the membership functions and prune additional rules. The newly proposed rule pruning method can eliminate weak rules from the rule base using the concepts of support and confidence. The membership functions tuning process is accomplished using the tasks of core displacement and width alteration of the symmetric functions. MOKBL+MOMs and its stages were validated using 28 real-world datasets and compared with two state-of-the-art regression solutions through non-parametric statistical tests. The experimental results confirmed the effectiveness of MOKBL+MOMs in terms of interpretability (complexity), accuracy, and time. |
| Databáze: | OpenAIRE |
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