scholarly journals Fast rule-based bioactivity prediction using associative classification mining

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Pulan Yu ◽  
David J Wild
Keyword(s):  
Associative Classification ◽  
Rule Based ◽  
Bioactivity Prediction

ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

2021 ◽  
Vol 20 (01) ◽  
pp. 2150013
Author(s):  
Mohammed Abu-Arqoub ◽  
Wael Hadi ◽  
Abdelraouf Ishtaiwi
Keyword(s):  
Decision Making ◽  
Class Imbalance ◽  
The Other ◽  
Associative Classification ◽  
Rule Based ◽  
New Approach ◽  
New Novel ◽  
Accuracy Measure ◽  
High Level ◽  
Substantial Interest

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

scholarly journals A New Classification Approach Based on Multiple Classification Rules

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongmei Zhou
Keyword(s):  
Classification Rule ◽  
Classification Rules ◽  
Associative Classification ◽  
New Classification ◽  
Class Label ◽  
Rule Based ◽  
Classification Approach ◽  
Minimum Support ◽  
Multiple Classification ◽  
Rule Set

A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. It is difficult to select a high quality rule set for classification. Second, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence. In comparison with associative classification, some improved traditional rule-based classification approaches often produce a classification rule set that plays an important role in prediction. Thus, some improved traditional rule-based classification approaches not only achieve better efficiency than associative classification but also get higher accuracy. In this paper, we put forward a new classification approach called CMR (classification based on multiple classification rules). CMR combines the advantages of both associative classification and rule-based classification. Our experimental results show that CMR gets higher accuracy than some traditional rule-based classification methods.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Phishing Detection: A Case Analysis on Classifiers with Rules Using Machine Learning

2017 ◽  
Vol 16 (04) ◽  
pp. 1750034 ◽  
Author(s):  
Fadi Thabtah ◽  
Firuz Kamalov
Keyword(s):  
Predictive Models ◽  
Predictive Accuracy ◽  
Web Security ◽  
Rule Induction ◽  
Classification Rule ◽  
Associative Classification ◽  
Actual Performance ◽  
Rule Based ◽  
Pros And Cons ◽  
Phishing Detection

A typical predictive approach in data mining that produces If-Then knowledge for decision making is rule-based classification. Rule-based classification includes a large number of algorithms that fall under the categories of covering, greedy, rule induction, and associative classification. These approaches have shown promising results due to the simplicity of the models generated and the user’s ability to understand, and maintain them. Phishing is one of the emergent online threats in web security domains that necessitates anti-phishing models with rules so users can easily differentiate among website types. This paper critically analyses recent research studies on the use of predictive models with rules for phishing detection, and evaluates the applicability of these approaches on phishing. To accomplish our task, we experimentally evaluate four different rule-based classifiers that belong to greedy, associative classification and rule induction approaches on real phishing datasets and with respect to different evaluation measures. Moreover, we assess the classifiers derived and contrast them with known classic classification algorithms including Bayes Net and Simple Logistics. The aim of the comparison is to determine the pros and cons of predictive models with rules and reveal their actual performance when it comes to detecting phishing activities. The results clearly showed that eDRI, a recently greedy algorithm, not only generates useful models but these are also highly competitive with respect to predictive accuracy as well as runtime when they are employed as anti-phishing tools.

scholarly journals Performance Comparison of Rule Based Classification Algorithms

2011 ◽  
pp. 137-142
Author(s):  
Prafulla Gupta ◽  
Durga Toshniwal
Keyword(s):  
Performance Comparison ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
First Order ◽  
Rule Analysis ◽  
Basic Ideas ◽  
Predictive Rule

Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. CPAR inherits the basic ideas of FOIL (First Order Inductive Learner) algorithm and PRM (Predictive Rule Mining) algorithm in rule generation. It integrates the features of associative classification in predictive rule analysis. In comparison of FOIL, PRM algorithm usually generates more rules. PRM uses concept of lowering weights rather than removing tuple if tuple is satisfied by the rule. The distinction between CPAR and PRM is that instead of choosing only the attribute that displays the best gain on each iteration CPAR may choose a number of attributes if those attributes have gain close to best gain.

Using Conventional Articulation Tests With Highly Unintelligible Children

1992 ◽  
Vol 23 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Pamela G. Garn-Nunn ◽  
Vicki Martin
Keyword(s):  
Test Results ◽  
Phonological Processes ◽  
Rule Based ◽  
Severity Level ◽  
Error Sensitivity ◽  
Conventional Tests ◽  
Severity Levels ◽  
Conventional Test ◽  
Impaired Children

This study explored whether or not standard administration and scoring of conventional articulation tests accurately identified children as phonologically disordered and whether or not information from these tests established severity level and programming needs. Results of standard scoring procedures from the Assessment of Phonological Processes-Revised, the Goldman-Fristoe Test of Articulation, the Photo Articulation Test, and the Weiss Comprehensive Articulation Test were compared for 20 phonologically impaired children. All tests identified the children as phonologically delayed/disordered, but the conventional tests failed to clearly and consistently differentiate varying severity levels. Conventional test results also showed limitations in error sensitivity, ease of computation for scoring procedures, and implications for remediation programming. The use of some type of rule-based analysis for phonologically impaired children is highly recommended.

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