.png){kind=icon}
返回
Classification rule discovery using learning automata
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-011-0056-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning automata (LA) are adaptive decision making units that can learn to choose the optimal action from a set of actions by interaction with an environment (search space). This article proposes the use of the learning automata as a new tool for data mining (namely LA-miner). The basic scheme is utilizing LA as an effective optimizer for searching the rule-set space. In fact, LA-miner searches the rule-set space to discover an effective rule-set which maximizes a predefined fitness function. The fitness function is related to the total true positives, false positives, true negatives, and false negatives. Extensive experimental results on different kinds of benchmarks with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. The comparative results illustrate that the performance of the proposed LA-miner is comparable to, sometimes better than those of the CN2 (a traditional data mining method) and similar approaches which are designed based on the swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and an evolutionary algorithm (genetic algorithm).
Keyword:
Learning automata
Rule based classifiers
Data mining
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
暂无机构信息
引用论文
A Role for Transcription Factor GTF2IRD2 in Executive Function in Williams-Beuren Syndrome.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Advancing Volcanic Activity Monitoring: A Near-Real-Time Approach with Remote Sensing Data Fusion for Radiative Power Estimation火山活动监测的推进：一种基于遥感数据融合的近实时辐射功率估计方法
OAAI
OAAI分享收藏
查看更多内容