.png){kind=icon}
返回
A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data
.png){kind=icon}
PRE
AI
DOI：10.1007/s12559-018-9617-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The state-of-the-art in associative classification includes interesting approaches for building accurate and interpretable classifiers. These approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building), some of them being computational expensive. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): (1) mining reliable association rules; (2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on different architectures (multi-thread, Apache Spark and Apache Flink) to take advantage of the distributed computing. The experimental results have been obtained on 40 well-known datasets and analyzed through non-parametric tests. Results were compared to multiple approaches in the field and analyzed on three ways: quality of the predictions, level of interpretability, and efficiency. The proposed method obtained accurate and interpretable classifiers in an efficient way even on high-dimensional data, outperforming the state-of-the-art algorithms on three different levels: quality of the predictions, interpretability, and efficiency.
Keyword:
Big data
Associative classification
Evolutionary computation
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:
4.3
论文数:
1.6K
被引数:
3.6K
机构
引用论文
.png){kind=icon}
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning基于遗传规则选择和横向调整的高维问题模糊关联规则分类模型
OAAI
OAAI分享收藏
查看更多内容