TY - GEN
T1 - A Taxation attribute reduction based on genetic algorithm and rough set theory
AU - Linzhang, Xu
AU - Zhen, Han
AU - Yanning, Zhang
PY - 2008
Y1 - 2008
N2 - Selection of Taxation attributes is one difficult question in analyzing the sources of taxation. This paper introduces genetic-algorithm-based rough set attribute reduction algorithm into the job of taxation attribute reduction. By referring to the concept of dependability in rough set, this method optimizes the configuration of fitness function, improves the convergence of original algorithm and changes the limitation of current attribute reduction in genetic algorithm. This algorithm fundamentally realizes the selection of comparatively small attribute sets with the presupposition that the data classification ability is not changed. It is valid after being tested.
AB - Selection of Taxation attributes is one difficult question in analyzing the sources of taxation. This paper introduces genetic-algorithm-based rough set attribute reduction algorithm into the job of taxation attribute reduction. By referring to the concept of dependability in rough set, this method optimizes the configuration of fitness function, improves the convergence of original algorithm and changes the limitation of current attribute reduction in genetic algorithm. This algorithm fundamentally realizes the selection of comparatively small attribute sets with the presupposition that the data classification ability is not changed. It is valid after being tested.
UR - https://www.scopus.com/pages/publications/67249140979
U2 - 10.1109/ICOSP.2008.4697749
DO - 10.1109/ICOSP.2008.4697749
M3 - 会议稿件
AN - SCOPUS:67249140979
SN - 9781424421794
T3 - International Conference on Signal Processing Proceedings, ICSP
SP - 2884
EP - 2887
BT - 2008 9th International Conference on Signal Processing, ICSP 2008
T2 - 2008 9th International Conference on Signal Processing, ICSP 2008
Y2 - 26 October 2008 through 29 October 2008
ER -