.png){kind=icon}
返回
A new fast associative classification algorithm for detecting phishing websites
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.08.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the Fl evaluation measures. The results indicate that FACA is very successful with regard to the Fl evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Associative classification
Phishing websites
Classification
Data mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
A novel associative classification model based on a fuzzy frequent pattern mining algorithm基于模糊频繁模式挖掘算法的关联分类模型
OAAI
OAAI分享收藏Physiological and behavioral responses to corticotropin-releasing factor administration: is CRF a mediator of anxiety or stress responses?促肾上腺皮质激素释放因子的生理和行为反应: CRF是焦虑或应激反应的中介物吗？
PREAI
分享收藏La convention sur la diversité biologique : tentative de bilan depuis le sommet de Rio de Janeiro.png)
VertigO
OAAI
VertigOIF0
OAAI分享收藏 没有更多内容
没有更多内容