A new sequential classification to assist Ad auction agent in making decisions

Mesbah, Sara; Taghiyareh, Fattaneh

doi:10.1109/istel.2010.5734168

Cited by 5 publications

(4 citation statements)

References 21 publications

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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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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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“…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%

“…More importantly, there are different definitions of the most important concept in NSP mining -negative containment -which defines whether a data sequence contains a negative sequence (see more details in Section 4.2). Some definitions are more generic and typical [11,12,38,39] than others which either incorporate additional constraints on negation format and containment [21][22][23][25][26][27][28][29] or offer no clear definition [18,31].…”

Section: Introductionmentioning

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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“…A vital ability of intelligent agents is making rational, accurate and quick decisions in dynamic For this classification, data log history of TAC/AD sales is used. Agents that are equipped with classifiers can gain higher profit comparing to those that are not so [17]. Chen has designed and implemented a multi user and multi access marketplace in which the users can access the system through the web, devices equipped with wireless applicable protocol (WAP) and agents.…”

Section: Introductionmentioning

confidence: 99%