Compact Representations of Sequential Classification Rules

Baralis, Elena; Chiusano, Silvia; Dutto, Riccardo; Mantellini, Luigi

doi:10.1007/978-3-540-78488-3_1

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…However, genes interact with each other through various pathways and regulative networks, in which case the order, instead of unordered co-occurrence, among items seems more exploitable. Correspondingly, sequential classification rules was introduced [19], which profitably exploit the correlation among the ordered items for contrast analysis [20,21]. However, mining the full set of sequence/sequential patterns is not only time-consuming and but also uselessness.According to Occam's razor principle [7], too many rules may reduce the generalization performance of a sample classifier.…”

Section: Related Workmentioning

confidence: 99%

Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

Zhao

Wang

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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Diagnostic genes refer to the genes closely related to a specific disease phenotype, the powers of which to distinguish between different classes are often high. Most methods to discovering the powerful diagnostic genes are either singleton discriminability-based or combination discriminability-based. However, both ignore the abundant interactions among genes, which widely exist in the real world.In this paper, we tackle the problem from a new point of view and make the following contributions: (1) we propose an EWave model, which profitably exploits the ordered expressions among genes based on the defined equivalent dimension group sequences taking into account the "noise" universal in the real data;(2) we devise a novel sequence rule, namely interesting non-redundant contrast sequence rule, which is able to capture the difference between different phenotypes in a high accuracy using as few as possible genes; (3) we present an efficient algorithm called NRMINER to find such rules. Unlike the conventional column enumeration and the more recent rowenumeration, it performs a novel template-driven enumeration by making use of the special characteristic of microarray data modeled by EWave. Extensive experiments conducted on various synthetic and real datasets show that: (1) NRMINER is significantly faster than the competing algorithm by up to about one order of magnitude; (2) it provides a higher accuracy using fewer genes. Many diagnostic genes discovered by NRMINER are proved biologically related to some disease.

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Section: Related Workmentioning

confidence: 99%

Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

Zhao

Wang

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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“…Mining sequential patterns [20] is a NP-hard problem. The good pruning properties of frequency measure and condensed representations of frequent patterns [21] allows to save computational time though the problem remains hard for large-scale data sets (see [22] for the case of sequential classification rules). Our level evaluation criterion does not hold as good properties as the frequency.…”

Section: Mining Sequential Classification Rulesmentioning

confidence: 99%

A Parameter-Free Approach for Mining Robust Sequential Classification Rules

Egho

Boullé

Voisine

et al. 2015

2015 IEEE International Conference on Data Mining

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Sequential data is generated in many domains of science and technology. Although many studies have been carried out for sequence classification in the past decade, the problem is still a challenge; particularly for pattern-based methods. We identify two important issues related to pattern-based sequence classification which motivate the present work: the curse of parameter tuning and the instability of common interestingness measures. To alleviate these issues, we suggest a new approach and framework for mining sequential rule patterns for classification purpose. We introduce a space of rule pattern models and a prior distribution defined on this model space. From this model space, we define a Bayesian criterion for evaluating the interest of sequential patterns. We also develop a parameter-free algorithm to efficiently mine sequential patterns from the model space. Extensive experiments show that (i) the new criterion identifies interesting and robust patterns, (ii) the direct use of the mined rules as new features in a classification process demonstrates higher inductive performance than the state-of-the-art sequential pattern based classifiers.

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“…Closed patterns were successfully extended to sequence in [26], [37], [38]. Recently, generator sequences were proposed in [24], [25], [39]. Subsequently, a general framework for minimal pattern mining was introduced by Soulet et al in [40], but this was limited to chains (i.e., sequences without gaps).…”

Section: Related Workmentioning

confidence: 99%

Sequence Classification Based on Delta-Free Sequential Patterns

Holat

Plantevit

Raïssi

et al. 2014

2014 IEEE International Conference on Data Mining

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Sequential pattern mining is one of the most studied and challenging tasks in data mining. However, the extension of well-known methods from many other classical patterns to sequences is not a trivial task. In this paper we study the notion of δ-freeness for sequences. While this notion has extensively been discussed for itemsets, this work is the first to extend it to sequences. We define an efficient algorithm devoted to the extraction of δ-free sequential patterns. Furthermore, we show the advantage of the δ-free sequences and highlight their importance when building sequence classifiers, and we show how they can be used to address the feature selection problem in statistical classifiers, as well as to build symbolic classifiers which optimizes both accuracy and earliness of predictions.

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Compact Representations of Sequential Classification Rules

Cited by 4 publications

References 22 publications

Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules

A Parameter-Free Approach for Mining Robust Sequential Classification Rules

Sequence Classification Based on Delta-Free Sequential Patterns

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