| Full text | |
| Author(s): |
Barros, Rodrigo C.
[1]
;
Basgalupp, Marcio P.
[2]
;
Freitas, Alex A.
[3]
;
de Carvalho, Andre C. P. L. F.
[4]
Total Authors: 4
|
| Affiliation: | [1] Pontificia Univ Catolica Rio Grande do Sul, Fac Informat, BR-90619900 Porto Alegre, RS - Brazil
[2] Univ Fed Sao Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos - Brazil
[3] Univ Kent, Dept Comp Sci, Canterbury CT2 7NF, Kent - England
[4] Univ Sao Paulo, Dept Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; v. 18, n. 6, p. 873-892, DEC 2014. |
| Web of Science Citations: | 18 |
| Abstract | |
Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure. (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 |