Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99521
Title: Fragmentation problem and automated feature construction
Authors: Setiono, Rudy 
Liu, Huan 
Issue Date: 1998
Citation: Setiono, Rudy,Liu, Huan (1998). Fragmentation problem and automated feature construction. Proceedings of the International Conference on Tools with Artificial Intelligence : 208-215. ScholarBank@NUS Repository.
Abstract: Selective induction algorithms are efficient in learning target concepts but inherit a major limitation - each time only one feature is used to partition the data until the data is divided into uniform segments. This limitation results in problems like replication, repetition, and fragmentation. Constructive induction has been an effective means to overcome some of the problems. The underlying idea is to construct compound features that increase the representation power so as to enhance the learning algorithm 's capability in partitioning data. Unfortunately, many constructive operators are often manually designed and choosing which one to apply poses a serious problem itself. We propose an automatic way of constructing compound features. The method can be applied to both continuous and discrete data and thus all the three problems can be eliminated or alleviated. Our empirical results indicate the effectiveness of the proposed method.
Source Title: Proceedings of the International Conference on Tools with Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/99521
ISSN: 10636730
Appears in Collections:Staff Publications

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