Multi-Objective Optimization-Based Networked Multi-Label Active Learning

Multi-Objective Optimization-Based Networked Multi-Label Active Learning

Lei Li (Hefei University of Technology, Hefei, China), Yuqi Chu (Luoyang Optoelectro Technology Development Center, Luoyang, China), Guanfeng Liu (Macquarie University, NSW, Australia) and Xindong Wu (Mininglamp Academy of Sciences, Mininglamp Technologies, Beijing, China)
Copyright: © 2019 |Pages: 26
DOI: 10.4018/JDM.2019040101

Abstract

Along with the fast development of network applications, network research has attracted more and more attention, where one of the most important research directions is networked multi-label classification. Based on it, unknown labels of nodes can be inferred by known labels of nodes in the neighborhood. As both the scale and complexity of networks are increasing, the problems of previously neglected system overhead are turning more and more seriously. In this article, a novel multi-objective optimization-based networked multi-label seed node selection algorithm (named as MOSS) is proposed to improve both the prediction accuracy for unknown labels of nodes from labels of seed nodes during classification and the system overhead for mining the labels of seed nodes with third parties before classification. Compared with other algorithms on several real networked data sets, MOSS algorithm not only greatly reduces the system overhead before classification but also improves the prediction accuracy during classification.
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Introduction

With the fast development of network applications (Batini & Rula, 2015; Cao et al., 2016; Long & Siau, 2007), network research has attracted more attention from both academic researchers and industrial engineers (Bhagat, Cormode, & Muthukrishnan, 2011; Bu et al., 2018; Li, et al., 2016), where one of the important research directions is networked multi-label classification (Wang & Sukthankar, 2013; Wu, Zhao, & Li, 2016; Zhang, et al., 2010). Specifically, unknown labels of nodes can be inferred by the known labels of other nodes in the neighborhood, and these inferred labels can be further used in user classification, community detection, or personalized recommendation (Guo, et al., 2017; Hong, et al., 2015; Li, et al., 2015). Networked data, which is different from traditional data with simple structures (Bhagat, Cormode, & Muthukrishnan, 2011), can reflect the complex relations, such as friendship or colleagues in life, or co-authorship of an article (Guo, et al., 2017; Miller, Perlman, & Brehm, 2007), between nodes in network environments (Fakhraei, et al., 2015; Otte & Rousseau, 2002), which makes it difficult to classify labels in networks (McDowell & Aha, 2016).

Mathematically, the multi-labeled network can be represented as a graph G=(W, E, C, Y), where W={JDM.2019040101.m01, JDM.2019040101.m02,..., JDM.2019040101.m03} is a set of nodes, E is a set of edges that connect pairs of nodes, C={JDM.2019040101.m04, JDM.2019040101.m05,..., JDM.2019040101.m06} represents labels with K classes, and JDM.2019040101.m07=(JDM.2019040101.m08)∈JDM.2019040101.m09 denotes the multi-labels that are associated with node JDM.2019040101.m10 (if JDM.2019040101.m11 belongs to label JDM.2019040101.m12, JDM.2019040101.m13 = 1, otherwise JDM.2019040101.m14= 0). W is divided into two disjoint parts: S, i.e., nodes whose labels are known which are named as seed nodes, and S = nseed and U, i.e., nodes whose labels need to be classified. The networked multi-label classification problem is to use S to infer the labels for nodes in U. Our seed node selection is to actively learn the set S to satisfy some objectives under certain conditions.

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