Please use this identifier to cite or link to this item:

Title: A knowledge-acquisition strategy based on genetic programming
Authors: Kuo, Chan-Sheng;Hong, T.-P.;Chen, C.-L.
Contributors: 資管系
Keywords: Classification (of information);Computer programming;Genetic algorithms;Genetic programming;Information technology;Separation;Technology;Technology transfer;Acquisition strategies;Breast cancers;Classification rules;Classification trees;Evolutionary processes;Genetic operators;International conferences;Knowledge creations;Knowledge evolution;Learning strategies;Sub trees;Three phases;Knowledge acquisition
Date: 2007
Issue Date: 2015-07-13 16:29:13 (UTC+8)
Abstract: In this paper, we have modified our previous GP-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge evolution, and knowledge output. One new genetic operator, separation, is designed in the proposed approach to remove contradiction, thus producing more accurate classification rules. A subtree pruning technique is also used to restrain the classification trees excessively expanding in the evolutionary process. Experimental results from diagnosis of breast cancers also show the feasibility of the proposed algorithm. © 2007 IEEE.
Relation: 2007 International Conference on Convergence Information Technology, ICCIT 2007
2nd International Conference on Convergent Information Technology, ICCIT 07,21 November 2007 through 23 November 2007,Gyongju
Data Type: conference
DOI 連結:
Appears in Collections:[資訊管理學系] 會議論文

Files in This Item:

File Description SizeFormat

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing