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.

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.

Evolution of Genetic Algorithms in Classification Rule Mining

2013 ◽  
pp. 328-363 ◽  
Author(s):  
Dipankar Dutta ◽  
Jaya Sil
Keyword(s):  
Data Mining ◽  
Soft Computing ◽  
Computing Methods ◽  
Classification Rule ◽  
Classification Rules ◽  
Rule Mining ◽  
Rule Set ◽  
Soft Computing Methods ◽  
Over Time

Classification is one of the most studied areas of data mining, which gives classification rules during training or learning. Classification rule mining, an important data-mining task, extracts significant rules for classification of objects. In this chapter class specific rules are represented in IF <Antecedent> THEN <Consequent> form. With the popularity of soft computing methods, researchers explore different soft computing tools for rule discovery. Genetic algorithm (GA) is one of such tools. Over time, new techniques of GA for forming classification rules are invented. In this chapter, the authors focus on an understanding of the evolution of GA in classification rule mining to get an optimal rule set that builds an efficient classifier.

scholarly journals Comparison of spatial classification rules with different conditional distributions of class label

2014 ◽  
Vol 19 (1) ◽  
pp. 109-117 ◽  
Author(s):  
Giedrius Stabingis ◽  
Kęstutis Dučinskas ◽  
Lijana Stabingienė
Keyword(s):  
Supervised Classification ◽  
Training Sample ◽  
Classification Rule ◽  
Correlated Noise ◽  
Classification Rules ◽  
Class Label ◽  
Spatial Classification ◽  
Spatially Correlated ◽  
Method Accuracy ◽  
Supervised Classification Methods

In this paper spatial classification rules based on Bayes discriminant functions are considered. The novelty of this work is that the statistical supervised classification method is improved by extending the influence of spatial correlation between observation to be classified and training sample. Such methods are used for data containing spatially correlated noise. Method accuracy is tested experimentally on artificially corrupted images. This classification rule with distance based conditional distribution for class label shows advantage against other classification rule ignoring such influence and against other commonly used supervised classification methods.

Plant Traits Response to Grazing Exclusion by Fencing Assessed via Multiple Classification Approach: A Case from a Subalpine Meadow

2019 ◽  
Vol 67 (1) ◽  
pp. 33 ◽  
Author(s):  
Wen Jin Li ◽  
Shuang Shuang Liu ◽  
Jin Hua Li ◽  
Ru Lan Zhang ◽  
Ka Zhuo Cai Rang ◽  
...  
Keyword(s):  
Plant Traits ◽  
Classification Approach ◽  
Grazing Exclusion ◽  
Subalpine Meadow ◽  
Multiple Classification

scholarly journals gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

2021 ◽  
Vol 11 (6) ◽  
pp. 2511
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R. Muhammad Atif Azad
Keyword(s):  
State Of The Art ◽  
Heuristic Method ◽  
Good Explanation ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Models ◽  
Boundary Values ◽  
Class Label ◽  
Input Space ◽  
Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

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.

Ansa cervicalis — A new classification approach

2019 ◽  
Vol 222 ◽  
pp. 55-60
Author(s):  
Anatoly I. Shvedavchenko ◽  
Marine V. Oganesyan ◽  
Christian M. Hammer ◽  
Friedrich Paulsen ◽  
Anastasia A. Bakhmet
Keyword(s):  
New Classification ◽  
Classification Approach ◽  
Ansa Cervicalis
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