| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Pontificia Univ Catolica Rio Grande Sul PUCRS, Fac Informat FACIN, Porto Alegre, RS - Brazil
[2] Univ Fed Sao Paulo UNIFESP, ICT, Sao Jose Dos Campos - Brazil
[3] Univ Sao Paulo, ICMC, Sao Carlos, SP - Brazil
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | Genetic Programming and Evolvable Machines; v. 16, n. 3, p. 241-281, SEP 2015. |
| Web of Science Citations: | 1 |
| Abstract | |
In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance. (AU) | |
| FAPESP's process: | 09/14325-3 - A meta-learner for model trees induction through Genetic Programming |
| Grantee: | Rodrigo Coelho Barros |
| Support Opportunities: | Scholarships in Brazil - Doctorate |