Comparative Performance of Interestingness Measures to Identify Redundant and Non-informative Rules from Web Usage Data

Sisodia, Dilip Singh; Singhal, Riya; Kandal, Vijay

doi:10.14716/ijtech.v9i1.1510

Cited by 2 publications

(2 citation statements)

References 14 publications

(16 reference statements)

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“…Interestingness is difficult to define quantitatively [3] but most interestingness measures are classified in objective measures and subjective measures. Objective measures are domain-independent, one of them is the interestingness which is expressed in terms of statistic or information theory applied over the database.…”

Section: Related Workmentioning

confidence: 99%

Knowledge redundancy approach to reduce size in association rules

Vera

Ortiz

Molina

et al. 2020

IJCAI

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Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovered models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist in the decisionmaking process. The previous handicap is hightened in the presence of redundant rules in the final set. In this work, a new definition of redundancy in association rules is proposed, based on user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows finding more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90 percent of all generated rules, using prior knowledge always below ten percent. So, our method improves the efficiency of association rules mining and the exploitation of discovered association rules.Povzetek: Opisan je sistem za zmanjševanje števila in dolžine pravil s pomočjo analize redundantnosti za metode asociativnega učenja.

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Section: Related Workmentioning

confidence: 99%

Knowledge redundancy approach to reduce size in association rules

Vera

Ortiz

Molina

et al. 2020

IJCAI

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“…The advancement in data generation and acquisition tools and techniques has accelerated the growth and accessibility of raw data. This has further resulted in new avenues of learning from historical data (Sisodia et al, 2018b). The existing machine learning algorithms show good performance for many real-world applications with proportionate class instances.…”

Section: Introductionmentioning

confidence: 99%

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Sisodia¹,

Verma²

2019

IJTech

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Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. The proposed ML-ESB is designed for handling only the binary class imbalance problem. In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve. The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets.

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Comparative Performance of Interestingness Measures to Identify Redundant and Non-informative Rules from Web Usage Data

Cited by 2 publications

References 14 publications

Knowledge redundancy approach to reduce size in association rules

Knowledge redundancy approach to reduce size in association rules

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

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