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Heuristic creation of deep rule ensemble through iterative expansion of feature space

Liu, Han ORCID: and Chen, Shyi-Ming 2020. Heuristic creation of deep rule ensemble through iterative expansion of feature space. Information Sciences 520 , pp. 195-208. 10.1016/j.ins.2020.02.001

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Rule learning approaches, which essentially aim to gerenate a decision tree or a set of “if-then” rules, have been popularly used in practice for automatically building rule-based models for prediction tasks, e.g., classification and regression. The key strength of rule-based models is their ability to interpret how an output is obtained given an input, in comparison with models trained by other machine learning approaches, e.g., neural networks. Moreover, ensemble learning approaches have been adopted as a popular way for advancing the performance of rule-based prediction through producing multiple rule-based models with diversity. Traditional approaches of ensemble learning are typically designed to train a single ensemble. In recent years, there have been some studies on creation of multiple ensembles towards increasing the diversity among rule-based models and the depth of ensemble learning. In this paper, we propose a feature expansion driven approach for automatic creation of deep rule ensembles, i.e., the dimensionality of the feature space is increased at each iteration by adding features newly created at the previous iteration. The proposed approach is compared with more recent approaches of rule learning and ensemble creation. The experimental results show that the proposed approach achieves improved performance on various data sets.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Elsevier
ISSN: 0020-0255
Date of First Compliant Deposit: 17 February 2020
Date of Acceptance: 4 February 2020
Last Modified: 07 Nov 2023 19:24

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