Compact fuzzy association rule-based classifier

Pach, Ferenc Péter; Gyenesei, Attila; Abonyi, János

doi:10.1016/j.eswa.2007.04.005

Cited by 46 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system is much more effective and efficient than the current expert system used by major Australian banks. 3 $5000 1 PayAnyone $+5000 F raud t 4 $500 0 Bpay $+500 F raud t 5 $30 1 PayAnyone $+30 F raud t 6 $800 1 PayAnyone $-100 Genuine t 7 $3000 1 Bpay $+3000 F raud of maximal coverage gain on the large imbalanced data set. This paper is motivated by this challenging problem.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Selection of Globally Optimal Rules on Large Imbalanced Data Based on Rule Coverage Relationship Analysis

Wang

Cao

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Rule-based anomaly and fraud detection systems often suffer from massive false alerts against a huge number of enterprise transactions. A crucial and challenging problem is to effectively select a globally optimal rule set which can capture very rare anomalies dispersed in large-scale background transactions. The existing rule selection methods which suffer significantly from complex rule interactions and overlapping in large imbalanced data, often lead to very high false positive rate. In this paper, we analyze the interactions and relationships between rules and their coverage on transactions, and propose a novel metric, Max Coverage Gain. Max Coverage Gain selects the optimal rule set by evaluating the contribution of each rule in terms of overall performance to cut out those locally significant but globally redundant rules, without any negative impact on the recall. An effective algorithm, MCGminer, is then designed with a series of built-in mechanisms and pruning strategies to handle complex rule interactions and reduce computational complexity towards identifying the globally optimal rule set. Substantial experiments on 13 UCI data sets and a real time online banking transactional database demonstrate that MCGminer achieves significant improvement on both accuracy, scalability, stability and efficiency on large imbalanced data compared to several state-of-the-art rule selection techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

“…It results in a large volume of false alerts that cause expensive investigation fees. Therefore, we need to find the globally optimal rule set under specific criteria, rather than the non-optimal rule set proposed by approximate methods [2,3].…”

Section: Introductionmentioning

confidence: 99%

Efficient Selection of Globally Optimal Rules on Large Imbalanced Data Based on Rule Coverage Relationship Analysis

Wang

Cao

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…Over the past few years, different studies have proposed methods to obtain fuzzy association rule-based classifiers [23]- [28]. The task of classification is to find a set of rules in order to identify the classes of undetermined patterns.…”

Section: Fuzzy Association Rules For Classificationmentioning

confidence: 99%

“…In order to enhance the interpretability of the obtained classification rules and to avoid unnatural boundaries in the partitioning of the attributes, different studies have been presented to obtain classification systems, which is based on fuzzy association rules [23]- [28]. For instance, in [24], the authors have made use of a genetic algorithm (GA) [29], [30] to automatically determine minimum support and confidence thresholds, mining for each chromosome a fuzzy rule set for classification by means of an algorithm, which is based on the Apriori algorithm [31], and adjusting the fuzzy confidence of these rules with the approach that was proposed by Nozaki et al in [32].…”

mentioning

confidence: 99%

A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning

Alcalá‐Fdez

Alcalá

Herrera

2011

IEEE Trans. Fuzzy Syst.

318

203

View full text Add to dashboard Cite

Abstract-The inductive learning of fuzzy rule-based classification systems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. This growth makes the learning process more difficult and, in most cases, it leads to problems of scalability (in terms of the time and memory consumed) and/or complexity (with respect to the number of rules obtained and the number of variables included in each rule). In this paper, we propose a fuzzy association rulebased classification method for high-dimensional problems, which is based on three stages to obtain an accurate and compact fuzzy rule-based classifier with a low computational cost. This method limits the order of the associations in the association rule extraction and considers the use of subgroup discovery, which is based on an improved weighted relative accuracy measure to preselect the most interesting rules before a genetic postprocessing process for rule selection and parameter tuning. The results that are obtained more than 26 real-world datasets of different sizes and with different numbers of variables demonstrate the effectiveness of the proposed approach.

show abstract

“…Recently, many research works (Pach et al, 2008) also extend the associative classification to deal with numerical data by introducing the concept of fuzzy sets. Some others (Yan et al, 2009;Qodmanan et al, 2011) even use the genetic algorithm to learn the membership function of fuzzy logic or to mine the association rules without userspecified minimum support.…”

Section: Association Rule and Associative Classifiermentioning

confidence: 99%

Activity recognition with weighted frequent patterns mining in smart environments

Wen

Zhong

Wang

2015

Expert Systems with Applications

View full text Add to dashboard Cite

Compact fuzzy association rule-based classifier

Cited by 46 publications

References 22 publications

Efficient Selection of Globally Optimal Rules on Large Imbalanced Data Based on Rule Coverage Relationship Analysis

Efficient Selection of Globally Optimal Rules on Large Imbalanced Data Based on Rule Coverage Relationship Analysis

A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning

Activity recognition with weighted frequent patterns mining in smart environments

Contact Info

Product

Resources

About