Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns

Zhao, Yuhai; Zhang, Huaifeng; Wu, Shunqing; Pei, Jian; Cao, Longbing; Zhang, Chengqi; Bohlscheid, Hans

doi:10.1007/978-3-642-04174-7_42

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…Based on the work in [35], Zhao et al [36] also presented an approach for discovering both positive and negative impact-oriented sequential rules. Issues about sequence classification using positive and negative patterns were discussed in [25,37]. Positive and negative usage patterns are used in [19] to filter Web recommendation lists.…”

Section: Related Workmentioning

confidence: 99%

e-NSP: Efficient negative sequential pattern mining

Cao

Xiang

Zheng

2016

Artificial Intelligence

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As an important tool for behavior informatics, negative sequential patterns (NSP) (such as missing medical treatments) are critical and sometimes much more informative than positive sequential patterns (PSP) (e.g. using a medical service) in many intelligent systems and applications such as intelligent transport systems, healthcare and risk management, as they often involve non-occurring but interesting behaviors. However, discovering NSP is much more difficult than identifying PSP due to the significant problem complexity caused by non-occurring elements, high computational cost and huge search space in calculating negative sequential candidates (NSC). So far, the problem has not been formalized well, and very few approaches have been proposed to mine for specific types of NSP, which rely on database re-scans after identifying PSP in order to calculate the NSC supports. This has been shown to be very inefficient or even impractical, since the NSC search space is usually huge. This paper proposes a very innovative and efficient theoretical framework: set theory-based NSP mining (ST-NSP), and a corresponding algorithm, e-NSP, to efficiently identify NSP by involving only the identified PSP, without re-scanning the database. Accordingly, negative containment is first defined to determine whether a data sequence contains a negative sequence based on set theory. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The NSC supports are then calculated based only on the corresponding PSP. This not only avoids the need for additional database scans, but also enables the use of existing PSP mining algorithms to mine for NSP. Finally, a simple but efficient strategy is proposed to generate NSC. Theoretical analyses show that e-NSP performs particularly well on datasets with a small number of elements in a sequence, a large number of itemsets and low minimum support. e-NSP is compared with two currently available NSP mining algorithms via intensive experiments on three synthetic and six real-life datasets from aspects including data characteristics, computational costs and scalability. e-NSP is tens to thousands of times faster than baseline approaches, and offers a sound and effective approach for efficient mining of NSP in large scale datasets by directly using existing PSP mining algorithms.

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Section: Related Workmentioning

confidence: 99%

e-NSP: Efficient negative sequential pattern mining

Cao

Xiang

Zheng

2016

Artificial Intelligence

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“…Thus there is no need to re-scan the database after obtaining PSP. There exist some methods [44][45][46][47][48] which only use nSP or mine negative sequential rules, but do not involve nSP mining directly.…”

Section: Literature Reviewmentioning

confidence: 99%

Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Liu¹,

Lv²,

Xiang³

et al. 2017

JRST

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Positive and negative sequential patterns (PNSP) play an informative role in various applications. In this paper, a new method is proposed to effectively select the actionable sequential patterns (ASP) from the PNSPs by segmenting and discriminating elements with sequence. First, it is to locally discriminate adjacent elements and incremental elements in the PNSPs. Second, globally segment and discriminate all the elements with sequences. Third, Markov process is further applied to select the ASP by measuring the interestingness of a sequence. The experimental comparisons on synthetic and real-world databases show that the proposed method is very effective to select ASPs.

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“…Similarly, in negative sequence analysis, although typical algorithms including e-NSP and GA-NSP [32][33][34][35] incorporate one more relation, namely the negation of a sequential element, other couplings are overlooked, and there is no differentiation between sequences and/or between sequential elements.…”

Section: Classic Behavior Analysismentioning

confidence: 99%

Non-IIDness Learning in Behavioral and Social Data

Cao¹

2013

The Computer Journal

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Most of the classic theoretical systems and tools in statistics, data mining and machine learning are built on the fundamental assumption of IIDness, which assumes the independence and identical distribution of underlying objects, attributes and/or values. However, complex behavioral and social problems often exhibit strong couplings and heterogeneity between values, attributes and objects (i.e., non-IIDness). This fundamentally challenges the IIDness-based learning methodologies and techniques. This paper presents a high-level overview of the needs, challenges and opportunities of non-IIDness learning for handling complex behavioral and social problems. By reviewing the nature and issues of classic IIDness-based algorithms in frequent pattern mining, clustering and classification to complex behavioral and social applications, concepts, structures, frameworks and exemplar techniques are discussed for non-IIDness learning. Case studies, related work and prospects of non-IIDness learning are presented. Non-IIDness learning is also a fundamental issue in big data analytics.

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Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns

Cited by 20 publications

References 22 publications

e-NSP: Efficient negative sequential pattern mining

e-NSP: Efficient negative sequential pattern mining

Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Non-IIDness Learning in Behavioral and Social Data

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