Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Búza, Krisztián; Schmidt-Thieme, Lars

doi:10.1007/978-3-642-01044-6_9

Cited by 20 publications

(12 citation statements)

References 25 publications

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“…Research shows that the frequency of different motifs can be used to identify the domain of a network or the type of the network within a domain [28]. Motif classification studies are especially popular in biological networks [29][30][31][32].…”

Section: Motif Analysis Of Network Groupsmentioning

confidence: 99%

Identifying criminal organizations from their social network structures

Çinar¹,

Genç²,

Sever³

2019

Turk J Elec Eng & Comp Sci

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Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical resultsshow that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.

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Section: Motif Analysis Of Network Groupsmentioning

confidence: 99%

Identifying criminal organizations from their social network structures

Çinar¹,

Genç²,

Sever³

2019

Turk J Elec Eng & Comp Sci

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“…By reason of the increasing interest in time-series classification, various approaches have been introduced ranging from neural [10] and Bayesian networks [11] to genetic algorithms, support vector machines [12] and frequent pattern mining [13], [14]. However, the k-nearest neighbor (k-NN) classifier (especially for k = 1), has been shown to be competitive to many other, more complex models [5], [6], [7].…”

Section: Related Workmentioning

confidence: 99%

IQ estimation for accurate time-series classification

Búza

Νανόπουλος

Schmidt-Thieme

2011

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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Abstract-Due to its various applications, time-series classification is a prominent research topic in data mining and computational intelligence. The simple k-NN classifier using dynamic time warping (DTW) distance had been shown to be competitive to other state-of-the art time-series classifiers. In our research, however, we observed that a single fixed choice for the number of nearest neighbors k may lead to suboptimal performance. This is due to the complexity of time-series data, especially because the characteristic of the data may vary from region to region. Therefore, local adaptations of the classification algorithm is required. In order to address this problem in a principled way by, in this paper we introduce individual quality (IQ) estimation. This refers to estimating the expected classification accuracy for each time series and each k individually. Based on the IQ estimations we combine the classification results of several k-NN classifiers as final prediction. In our framework of IQ, we develop two time-series classification algorithms, IQ-MAX and IQ-WV. In our experiments on 35 commonly used benchmark data sets, we show that both IQ-MAX and IQ-WV outperform two baselines.

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“…In other words: the frequent itemsets (their presence or absence in each particular data object) are used as input features for statistical classifiers in order to predict some unknown properties of objects. This way, patterns have been used, for example, for time series classification [13,14,15]. In an other algorithm, frequent itemset mining have been used for building knowledge structures integrating expert knowledge and user vocabulary [16].…”

Section: Introductionmentioning

confidence: 99%

Towards better modeling of supermarkets

Búza

Buza

Kis

2010

2010 International Joint Conference on Computational Cybernetics and Technical Informatics

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-In this paper we introduce the supermarket setout problem which aims at finding an appropriate setout of products (i.e. which product should be placed on which position at which shelf) w.r.t. the expected profit, access time (time required to find a product), etc. Finding a good setout of products is important both in the customer-area and in the background store of supermarkets. The admissible setouts are constrained by laws (like food is not allowed to be placed next to chemicals) and "setout traditions" (even if two products are allowed to be placed next to each other by law, it may look "strange"). After taking all such requirements into account, there are usually still a great number of possible setouts that are generally greatly different from various aspects, like expected profit, customer satisfaction, or the time required to find a product. Therefore, finding an appropriate (closely optimal) solution is a crucial issue. Complex business problems, like the above supermarket setout problem, are often solved by means of artificial intelligence techniques that exploit results of advanced statistical analysis or data mining. In this paper, we develop a new algorithm for the supermarket setout problem. This is based on a combination of constraint satisfaction algorithms and frequent itemset mining (also known as market basket analysis).

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Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Cited by 20 publications

References 25 publications

Identifying criminal organizations from their social network structures

Identifying criminal organizations from their social network structures

IQ estimation for accurate time-series classification

Towards better modeling of supermarkets

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