Publication Type
Book Chapter
Version
publishedVersion
Publication Date
1-2008
Abstract
The use of a team of weak learners to learn a dataset has been shown better than the use of one single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be one of the best off-the-shelf classifiers. However, some problems still remain, including determining the optimal number of weak learners and the overfitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of genetic algorithm, weak learner and pattern distributor. In this paper, we revise the global search component by replacing it with a cluster-based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore formed, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Overfitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state-of-the-art methods.
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Engineering Evolutionary Intelligent Systems
Editor
ABRAHAM, Ajith; GROSAN, Crina; PEDRYCZ, Witold
First Page
157
Last Page
176
ISBN
9783540753957
Identifier
10.1007/978-3-540-75396-4_6
Publisher
Springer
City or Country
Germany
Citation
RAMANATHAN, Kiruthika and GUAN, Sheng Uei.
Enhancing recursive supervised learning using clustering and combinatorial optimization (RSL-CC). (2008). Engineering Evolutionary Intelligent Systems. 157-176.
Available at: https://ink.library.smu.edu.sg/sis_research/7394
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1007/978-3-540-75396-4_6