Parameter Optimization of Local-Concentration Model for Spam Detection by Using Fireworks Algorithm

摘要：

　　This paper proposes a new framework that optimizes anti-spam model with heuristic swarm intelligence optimization algorithms, and this framework could integrate various classifiers and feature extraction methods.In this framework, a swarm intelligence algorithm is utilized to optimize a parameter vector, which is composed of parameters of a feature extraction method and parameters of a classifier, considering the spam detection problem as an optimization process which aims to achieve the lowest error rate.Also, 2 experimental strategies were designed to objectively reflect the performance of the framework.Then, experiments were conducted, using the Fireworks Algorithm (FWA) as the swarm intelligence algorithm, the Local Concentration (LC) approach as the feature extraction method, and SVM as the classifier.Experimental results demonstrate that the framework improves the performance on the corpora PU1, PU2, PU3 and PUA, while the computational efficiency is applicable in real world.

关键词： Spam Detection Fireworks Algorithm Parameter Optimization Local Concentration Approach

作者: Wenrui He Guyue Mi Ying Tan

作者单位: Key Laboratory of Machine Perception (Ministry of Education),Department of Machine Intelligence, School of Electronics Engineering and Computer Science,Peking University, Beijing, 100871 P.R.China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 439-450

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）