Please use this identifier to cite or link to this item:
|Title:||Fragmentation problem and automated feature construction|
|Authors:||Setiono, Rudy |
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.