Evaluating associative classification algorithms for Big Data

Padillo, Francisco J.; Luna, José María; Ventura, Sebastián

doi:10.1186/s41044-018-0039-7

Cited by 19 publications

(14 citation statements)

References 28 publications

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“…During this step, the predictive power of the classifier is measured using prediction accuracy or error rate. In the last decade, multiple research studies, for example [4], [7] have reported the applicability of AC on various different applications including fraud detection, credit card scoring, bioinformatics, on-line security, medical diagnoses, text categorization and others. The wide spread of this classification approach is primarily due to the simplicity and interpretability of the rules in the classifier besides the high predictive accuracy of its classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Any potential rule that was unable to classify at least one training example is discarded. A number of successive AC algorithms have adopted the CBA database coverage method like CBA (2) [10] and ACCF [11], (uCBA) [12], X-class [13], SPARK-CBA [7], and others.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Padillo et al [7] evaluated a number of associative classifiers on 30 classic data sets and 10 big data sets (datasets with very high dimensionality). These algorithms including CBA and CPAR among others have been extended and implemented In big data platform such as SPARK and FLINK.…”

Section: Pearson Correlation Measurementioning

confidence: 99%

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New Associative Classification Method Based on Rule Pruning for Classification of Datasets

Rajab

2019

IEEE Access

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In data mining, a rule-based classification approach called Associative Classification (AC) normally builds accurate classifiers from supervised learning data sets. It extracts ''If-Then'' rules and associates each of the generated rules with two computed parameters; support and confidence. These two parameters are utilized to differentiate the rules' superiority during the building of a classifier's step. In current AC algorithms, whenever a rule is inserted into a classifier, all of its corresponding training data is discarded. However, the discarded data actually are used to compute support and confidence of other rules and will affect other lower ranked rules since rules normally have common training data examples. The use of static support and confidence may result in very large less-accurate classifiers.Thus, a procedure that amends other rules' support and confidence is important. This paper proposes a new procedure named Active Pruning Rules (APR) to overcome the above problem so then the classifiers' performance-especially predictive accuracy and reducing rule redundancy-will be further improved. The experimental results obtained from a number of University of California Irvine (UCI) data sets and real adult autism classification data set showed that APR is highly competitive to other AC and rule-based classifiers and often produces smaller yet more predictive classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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New Associative Classification Method Based on Rule Pruning for Classification of Datasets

Rajab

2019

IEEE Access

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“…DM contains a rich set of classification models; specifically, Support Vector Machine [9], Rule Based [10], Decision Tree [11,12], Bayesian classification [2], k -Nearest Neighbor [13], and AC [14]. Among all, AC is relatively new and promising [15,16,17,18,19,20,21,22,23,24] as it combines the best approaches of association rules mining (ARM) and classification. AC is based on ARM where, first, the strongest Class Association Rules (CAR) are discovered from dataset, followed by converting those rules into classifier model.…”

Section: Introductionmentioning

confidence: 99%

“…After the introduction of AC in 1997, numbers of algorithms are developed in this family e.g. CBA [14,25], CMAR [18], CPAR [21], MCAR [19], MAC [26], CMARAA [27], MRAC & MRAC+ [15], DAC [23], CBA-Spark and CPAR-Spark [24] and G3P-ACBD [28]. Almost all consist of three basic steps -a) Association rule generation, b) Classifier building -rule pruning and rule ranking c) Classification of unknown records using the classifier.…”

Section: Introductionmentioning

confidence: 99%

Associative Classification using Automata with Structure based Merging

Abrar¹,

Sim²,

Abbas³

2019

IJACSA

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Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rules. The process used to generate association rules is exponential by nature; thus in AC, researchers focused on the reduction of redundant rules via rules pruning and rules ranking techniques. These techniques take an important part in improving the efficiency; however, pruning may negatively affect the accuracy by pruning interesting rules. Further, these techniques are time consuming in term of processing and also require domain specific knowledge to decide upon the selection of the best ranking and pruning strategy. In order to overcome these limitations, in this research, an automata based solution is proposed to improve the classifier's accuracy while replacing ranking and pruning. A new merging concept is introduced which used structure based similarity to merge the association rules. The merging not only help to reduce the classifier size but also minimize the loss of information by avoiding the pruning. The extensive experiments showed that the proposed algorithm is efficient than AC, Naive Bayesian, and Rule and Tree based classifiers in term of accuracy, space, and speed. The merging takes the advantages of the repetition in the rules set and keep the classifier as small as possible.

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