A multi-objective evolutionary algorithm for mining quantitative association rules

Martín, Daniel; Rosete, Alejandro; Alcalá‐Fdez, Jesús; Herrera, Francisco

doi:10.1109/isda.2011.6121855

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…From the DM perspective, MOEAs are popular underlying optimisation solutions for a variety of DM tasks, enumerated as clustering (Kirkland et al, 2011;Ripon and Siddique, 2009), association rule mining (Matthews et al, 2011;Martín et al, 2011), classification Tan et al, 2014;Pangilinan and Janssens, 2011;Pourpanah et al, 2017), and feature selection (Tan et al, 2014;Venkatadri and Rao, 2010;Brester et al, 2014). For a complete review of the various MOEAs for DM, interested reader is referred to Mukhopadhyay et al (2014a, b).…”

Section: 1mentioning

confidence: 99%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach -A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.Findings -The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value -A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

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Section: 1mentioning

confidence: 99%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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“…Their main particularity lies on the use of genetic algorithms to mine fuzzy association rules. The authors in [29] went one step further and used, in addition to the three measures aforementioned, the amplitude of the intervals as well as the comprehensibility of the rule to form the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Selecting the best measures to discover quantitative association rules

et al. 2014

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The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.

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“…147 Determining suitable range has a direct effect on discovering 148 interesting and/or rare rules. We accomplish this in our 149 approach by introducing new definition of Non-Dominated 150 rules (Contrary to previous researches [10,14,15,21,24,25,32]). 151 In this new definition, we applied multi-objective concept to 152 find the most interesting state of a rule.…”

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confidence: 99%

“…Attributes of association rules can be placed in two 55 domains: the discrete and the continuous domain [4]. 56 Furthermore, association rules in the unsupervised domain can 57 be classified into two groups: categorical association rules versus 58 quantitative association rules [10,51] and frequent association 59 rules versus infrequent/rare rules [50]. There is also another type 60 of association rules, called class association rules.…”

mentioning

confidence: 99%

“…This strategy considers 139 an equal survival chance for individuals with different length. 140 However, previous researches [10,15,19,21,24,31,32] generate 141 their initial population randomly (rules with different lengths), 142 and it is possible that rules with a specific length do not appear 143 in the initial population. Hence, rare or interesting rules with 144 bigger length may not be produced by the evolutionary opera- 145 tors.…”

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confidence: 99%

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Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules

Almasi

Abadeh

2015

Knowledge-Based Systems

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A multi-objective evolutionary algorithm for mining quantitative association rules

Cited by 16 publications

References 16 publications

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Selecting the best measures to discover quantitative association rules

Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules

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