Distribution Rules with Numeric Attributes of Interest

Jorge, Alípio Mário; Azevedo, Paulo J.; Pereira, Fernando

doi:10.1007/11871637_26

Cited by 22 publications

(22 citation statements)

References 14 publications

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“…where μ S and μ 0 stand for the subgroup and database means of the target, respectively, and σ S denotes the standard deviation within the subgroup S. Other works consider the distribution of target values within the subgroup (Jorge et al 2006), and use statistical measures for assessing distributional differences. In the majority of these quality measures, the interestingness is computed from the distribution of the subgroup alone, or when compared to that of the entire dataset.…”

Section: Subgroup Discoverymentioning

confidence: 99%

Sports analytics for professional speed skating

Knobbe

Orie

Hofman

et al. 2017

Data Min Knowl Disc

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In elite sports, training schedules are becoming increasingly complex, and a large number of parameters of such schedules need to be tuned to the specific physique of a given athlete. In this paper, we describe how extensive analysis of historical data can help optimise these parameters, and how possible pitfalls of under-and overtraining in the past can be avoided in future schedules. We treat the series of exercises an athlete undergoes as a discrete sequence of attributed events, that can be aggregated in various ways, to capture the many ways in which an athlete can prepare for an important test event. We report on a cooperation with the elite speed skating team LottoNLJumbo, who have recorded detailed training data over the last 15 years. The aim of the project was to analyse this potential source of knowledge, and extract actionable and interpretable patterns that can provide input to future improvements in training. We present two alternative techniques to aggregate sequences of exercises into a combined, long-term training effect, one of which based on a sliding window, and one based on a physiological model of how the body responds to exercise. Next, we use both linear modelling and Subgroup Discovery to extract meaningful models of the data.

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Section: Subgroup Discoverymentioning

confidence: 99%

Sports analytics for professional speed skating

Knobbe

Orie

Hofman

et al. 2017

Data Min Knowl Disc

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“…The last key point meets the concept of distribution rule [17]. The consequent of a scrm is an empirical distribution over the classes as illustrated in the following example:…”

Section: Towards Modl Rulesmentioning

confidence: 99%

A Bayesian Approach for Classification Rule Mining in Quantitative Databases

Boullé

2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. We suggest a new framework for classification rule mining in quantitative data sets founded on Bayes theory -without univariate preprocessing of attributes. We introduce a space of rule models and a prior distribution defined on this model space. As a result, we obtain the definition of a parameter-free criterion for classification rules. We show that the new criterion identifies interesting classification rules while being highly resilient to spurious patterns. We develop a new parameter-free algorithm to mine locally optimal classification rules efficiently. The mined rules are directly used as new features in a classification process based on a selective naive Bayes classifier. The resulting classifier demonstrates higher inductive performance than state-of-the-art rule-based classifiers.

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“…This definition of classification rule is slightly different from the usual definition where the consequent is a class value. It refers to the notion of distribution rule [19] and allows us to access the whole frequency information within the contingency table of a rule π -which is needed for the development of our framework.…”

Section: Definition 2 (Standard Classification Rule Model)mentioning

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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Distribution Rules with Numeric Attributes of Interest

Cited by 22 publications

References 14 publications

Sports analytics for professional speed skating

Sports analytics for professional speed skating

A Bayesian Approach for Classification Rule Mining in Quantitative Databases

A Parameter-Free Approach for Mining Robust Sequential Classification Rules

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