.png){kind=icon}
返回
Data mining with an ant colony optimization algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2002.802452.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.
Keyword:
ant colony optimization
classification
data mining
knowledge discovery
期刊
IF:
12
论文数:
1.8K
被引数:
2.4W
机构
暂无机构信息
引用论文
Need for a consensus on the methods by which to measure joint mobility and the definition of norms for hypermobility that reflect age, gender and ethnic-dependent variation: is revision of criteria for joint hypermobility syndrome and Ehlers-Danlos syndrome hypermobility type indicated?.png)
Rheumatology
OAAI
RheumatologyIF0
OAAI分享收藏
查看更多内容