Fragmentation problem and automated feature construction

Rudy Setiono, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Tools with Artificial Intelligence
Editors Anon
Number of pages8
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1998 IEEE 10th International Conference on Tools with Artificial Intelligence - Taipei, China
Duration: Nov 10 1998Nov 12 1998


OtherProceedings of the 1998 IEEE 10th International Conference on Tools with Artificial Intelligence
CityTaipei, China

ASJC Scopus subject areas

  • Software


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