Adjusting and generalizing CBA algorithm to handling class imbalance

Chen, Wen-Chin; Hsu, Cheng–Che; Hsu, Jing-Ning

doi:10.1016/j.eswa.2011.11.113

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…Chen et al 65 proposed a Probabilistic Classification based on Association Rules (PCAR) to classify imbalanced data more correctly. PCAR performs changes in the pruning method, scoring procedure and rule sorting index of CBA to achieve such purpose.…”

Section: Cost-sensitive and Ensemble Approachesmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

108

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Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

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Section: Cost-sensitive and Ensemble Approachesmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

108

View full text Add to dashboard Cite

show abstract

“…Chen et al proposed a principal association mining (PAM) method to improve the accuracy and the size of classifier [4]. Some efficient methods were also proposed to improve the accuracy such as: using CBA to handle class imbalance [3] and uncertain datasets [10], methods that uses interestingness measures [11,27], a method that uses rule prioritization [5], and a method that uses closed sets [15].…”

Section: Mining Class Association Rulesmentioning

confidence: 98%

A Method for Query Top-K Rules from Class Association Rule Set

Nguyen

Nguyễn²,

et al. 2016

Intelligent Information and Database Systems

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Abstract. Methods for mining/querying Top-k frequent patterns and Top-k association rules have been developed in recent years. However, methods for mining/querying Top-k rules from a set of class association rules have not been developed. In this paper, we propose a method for querying Top-k class association rules based on the support. From the set of mined class association rules that satisfy the minimum support and minimum confidence thresholds, we use an insertion-based method to query Top-k rules. Firstly, we insert k rules from the rule set to the result set. After that, for each rule in the rest, we insert it into the result rule set using the idea of insertion strategy if its support is greater than the support of the last rule in the result rule set. Experimental results show that the proposed method is more efficient than obtaining the result after sorting the whole rule set.

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“…Associative classification (AC) integrates classification and association rule mining to construct classifier. Recent studies [8][9][10] have shown that AC approach has three concrete merits over other traditional classification approaches such as decision tree and rule induction in terms of accuracy and interpretability. Firstly, AC generates a simple "IF-THEN" rule, which enables the decision maker to easily manipulate and comprehend the outputted classifier.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Cao

Zhong

et al. 2020

IEEE Access

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Associative Classification (AC) is envisioned as one of the most attractive classification approaches to facilitate managers for prediction and decision making in a highly accurate and easily interpretable way. The most of existing AC algorithms mainly focus on the two static metrics of association rules: support and confidence. However, in this paper, we identify a potential limitation of the confidence and further point out these AC algorithms merely consider the explicit expression of classification information and knowledge of Class Association Rules (CARs) and neglect the implicit expression of classification information and knowledge of CARs. And even the explicit expression of classification information and knowledge of CARs is equivalent, the implicit expression of classification information and knowledge of them may be different. Thus, these CARs will cause different influence on prediction or decision-making in terms of the interpretability and rationality of classification, and will result in predictive bias or decision-making bias in practice. In response to this drawback, we introduce the notion of information entropy and propose an innovative approach for associative classification based on information entropy of frequent attribute set, named EAC. Different from the existing schemes, the proposed EAC algorithm enjoys the following promising merits: (1) lower predictive bias or decision-making bias by levering the information entropy of frequent attribute set; (2) better interpretability and rationality by setting higher support threshold; (3)setting global optimum parameter dynamically through repeated trials with reducing the dimension of data sets. Experiments on 20 well-known benchmark data sets demonstrate that our EAC approach is highly competitive to other state-of-the-art AC algorithms in terms of predictive bias or decision-making bias, interpretability, and efficiency, which can be used to construct a new classifier efficiently and effectively in many realistic scenarios. INDEX TERMS Data mining, associative classification, information entropy, frequent attribute set.

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Adjusting and generalizing CBA algorithm to handling class imbalance

Cited by 13 publications

References 16 publications

A review on classification of imbalanced data for wireless sensor networks

A review on classification of imbalanced data for wireless sensor networks

A Method for Query Top-K Rules from Class Association Rule Set

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

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