Multi-label lazy learning approach based on firefly method

doi:10.11772/j.issn.1001-9081.2018109182

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1305-1311.DOI: 10.11772/j.issn.1001-9081.2018109182

• Artificial intelligence • Previous Articles Next Articles

Multi-label lazy learning approach based on firefly method

CHENG Yusheng^1,2, QIAN Kun¹, WANG Yibing^1,2, ZHAO Dawei¹

1. School of Computer and Information, Anqing Normal University, Anqing Anhui 246011, China;
2. University Key Laboratory of Intelligent Perception and Computing of Anhui Province(Anqing Normal University), Anqing Anhui 246011, China

Received:2018-11-13 Revised:2018-12-02 Online:2019-05-14 Published:2019-05-10
Supported by:
This work is partially supported by the Key Scientific Research Project for Universities in Anhui Province (KJ2017A352), the Research and Innovation Team Building Plan of Anqing Normal University.

融合萤火虫方法的多标签懒惰学习算法

程玉胜^1,2, 钱坤¹, 王一宾^1,2, 赵大卫¹

1. 安庆师范大学计算机与信息学院, 安徽安庆 246011;
2. 安徽省高校智能感知与计算重点实验室(安庆师范大学), 安徽安庆 246011

通讯作者: 程玉胜
作者简介:程玉胜(1969-),男,安徽安庆人,教授,博士,主要研究方向:大数据、粗糙集、特征选择的机器学习;钱坤(1995-),男,安徽滁州人,硕士研究生,CCF会员,主要研究方向:多标签学习、机器学习、数据统计;王一宾(1970-),男,安徽安庆人,教授,硕士,CCF会员,主要研究方向:多标签学习、机器学习、软件安全;赵大卫(1993-),男,安徽芜湖人,硕士研究生,主要研究方向:机器学习、大数据、数据统计。
基金资助:
安徽省高校重点科研项目（KJ2017A352）；安庆师范大学科研创新团队建设计划。

Abstract

Abstract: The existing Improved Multi-label Lazy Learning Approach (IMLLA) has the problem that the influence of similarity information is ignored with only the neighbor label correlation information considered when the neighbor labels were used, which may reduce the robustness of the approach. To solve this problem, with firefly method introduced and the combination of similarity information with label information, a Multi-label Lazy Learning Approach based on FireFly method (FF-MLLA) was proposed. Firstly, Minkowski distance was used to measure the similarity between samples to find the neighbor point. Secondly, the label count vector was improved by combining the neighbor point and firefly method. Finally, Singular Value Decomposition (SVD) and kernel Extreme Learning Machine (ELM) were used to realize linear classification. The robustness of the approach was improved due to considering both label information and similarity information. The experimental results demonstrate that the proposed approach improves the classification performance to a great extent compared to other multi-label learning approaches. And the statistical hypothesis testing and stability analysis are used to further illustrate the rationality and effectiveness of the proposed approach.

Key words: multi-label learning, firefly method, label correlation, Improved Multi-label Lazy Learning Approach (IMLLA), Extreme Learning Machine (ELM)

摘要： 已有的多标签懒惰学习算法（IMLLA）在利用近邻标签时因仅考虑了近邻标签相关性信息，而忽略相似度的影响，这可能会使算法的鲁棒性有所降低。针对这个问题，引入萤火虫方法，将相似度信息与标签信息相结合，提出一种融合萤火虫方法的多标签懒惰学习算法（FF-MLLA）。首先，利用Minkowski距离来度量样本间相似度，从而找到近邻点；然后，结合标签近邻点和萤火虫方法对标签计数向量进行改进；最后，使用奇异值分解（SVD）与核极限学习机（ELM）进行线性分类。该算法同时考虑了标签信息与相似度信息从而提高了鲁棒性。实验结果表明，所提算法较其他的多标签学习算法有一定优势，并使用统计假设检验与稳定性分析进一步说明所提出算法的合理性与有效性。

关键词: 多标签学习, 萤火虫方法, 标签相关性, 多标签懒惰学习算法, 极限学习机

CLC Number:

TP181

CHENG Yusheng, QIAN Kun, WANG Yibing, ZHAO Dawei. Multi-label lazy learning approach based on firefly method[J]. Journal of Computer Applications, 2019, 39(5): 1305-1311.

程玉胜, 钱坤, 王一宾, 赵大卫. 融合萤火虫方法的多标签懒惰学习算法[J]. 计算机应用, 2019, 39(5): 1305-1311.

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Multi-label lazy learning approach based on firefly method

融合萤火虫方法的多标签懒惰学习算法

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