Wael Hadi

MAC: A Multiclass Associative Classification Algorithm

Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.

Categorize arabic data sets using multi-class classification based on association rule approach

Associative classification (AC) is a promising data mining approach which builds more accurate classifiers than traditional classification technique such as decision trees and rule induction. By integrating association rules mining with classification, AC has two main phases which are rule generation and classifier building. In this paper, we investigate one of the well known AC algorithm i.e. MCAR, Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that MCAR algorithm outperforms the NB and SVM algorithms with regards to all measures.

Derivation of Three Queue Nodes Discrete-Time Analytical Model Based on DRED Algorithm

This paper proposes a derivation of discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm to manage and control congestion in early stages before it occurs, which is referred to as the 3QN model. The proposed model consists of three interconnected queue processing nodes. Expressions were derived to calculate performance measures, namely; throughput, and average queuing delay. In order to investigate and analyze the effectiveness and flexibility of the proposed model, two scenarios were performed. These scenarios investigate the variation of packets arrival probability against throughput), and average queuing delay. Beside that we compare between the three queue nodes of the proposed model using the derived performance measures to identify which queue node provides better performance. Results show that queue node one has better performance than queue node two and three with regard to throughput. All the above performance measure results for the queue nodes are obtained only based on the queuing network setting parameters. Therefore the node which offers better performance results than others is solely based on the given values for the setting parameter.

PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING

Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confidence per Class, Full Match Rule) and one rule pruning procedure (Partial Matching) in AC. Furthermore, we review current prediction methods in AC. Experimental results on large English and Arabic text categorisation data collections (Reuters, SPA) using the proposed prediction methods and other popular classification algorithms (SVM, KNN, NB, BCAR, MCAR, C4.5, etc.), have been conducted. The bases of the comparison in the experiments are classification accuracy and the Break-Even-Point (BEP) evaluation measures. The results reveal that our prediction methods are very competitive with reference to BEP if compared with known AC prediction approaches such as those of 2-PS, ARC-BC and BCAR. Moreover, the proposed prediction methods outperform other existing methods in traditional classification approaches such as decision trees, and probabilistic with regards to accuracy. Finally, the results indicate that using the proposed pruning procedure in AC improved the accuracy of the outputted classifier.

Performance of NB and SVM classifiers in Islamic Arabic data

Text categorization is one of the well studied problems in data mining and information retrieval. Given a large quantity of documents in a data set where each document is associated with its corresponding category. Categorization involves building a model from classified documents, in order to classify previously unseen documents as accurately as possible. This paper investigates Naïve Bayesian method (NB) and Support Vector Machine (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorisation data sets reveal that SVM algorithm outperforms the NB with regards to all measures.

A new fast associative classification algorithm for detecting phishing websites

Display Omitted A new fast Associative classification mining approach is developed.The applicability of well-known associative classification techniques on detecting phishing websites is investigated.Experimental results using different associative classification algorithms was performed. Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the F1 evaluation measures. The results indicate that FACA is very successful with regard to the F1 evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure.

Multi-Label Rules Algorithm Based Associative Classification

Current associative classification (AC) algorithms generate only the most obvious class linked with a rule in the training data set and ignore all other classes. We handle this problem by proposing a learning algorithm based on AC called Multi-label Classifiers based Associative Classification (MCAC) that learns rules associated with multiple classes from single label data. MCAC algorithm extracts classifiers from the whole training data set discovering all possible classes connected with a rule as long as they have sufficient training data representation. Another distinguishing feature of the MCAC algorithm is the classifier building method that cuts down the number of rules treating one known problem in AC mining which is the exponential growth of rules. Experimentations using real application data related to a complex scheduling problem known as the trainer timetabling problem reveal that MCACs predictive accuracy is highly competitive if contrasted with known AC algorithms.

ACPRISM: Associative classification based on PRISM algorithm

Abstract Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from association rules two major drawbacks resulting in a massive set of generated rules, in addition to a very large number of models (classifiers). In response to these two drawbacks, a new AC algorithm based on PRISM algorithm (ACPRISM) is proposed which employs the power of the PRISM algorithm to decrease the number of generated rules. To investigate the efficiency and the performance of the proposed algorithm, five different algorithms were tested, namely FACA, CBA, MAC, PRISM and RIPPER. Two experiments were conducted on groundwater and 16 different well-known datasets using predictive accuracy (%), number of generated rules and time taken to build the model (learning times). Our experimental results show that the ACPRISM produced the lowest number of rules, and is much more efficient and more scalable than all considered algorithms with regard to learning times. Finally, the ACPRISM outperformed the CBA, MCAR, PRISM and RIPPER algorithms in terms of predictive accuracy, and produced comparable results to the FACA algorithm.

A new rule pruning text categorisation method

Associative classification integrates association rule and classification in data mining to build classifiers that are highly accurate than that of traditional classification approaches such as greedy and decision tree. However, the size of the classifiers produced by associative classification algorithms is usually large and contains insignificant rules. This may degrade the classification accuracy and increases the classification time, thus, pruning becomes an important task. In this paper, we investigate the problem of rule pruning in text categorisation and propose a new rule pruning techniques called High Precedence. Experimental results show that HP derives higher quality and more scalable classifiers than those produced by current pruning methods (lazy and database coverage). In addition, the number of rules generated by the developed pruning procedure is often less than that of lazy pruning.

Integrating associative rule-based classification with Naïve Bayes for text classification

Abstract Associative classification (AC) integrates the task of mining association rules with the classification task to increase the efficiency of the classification process. AC algorithms produce accurate classification and generate easy to understand rules. However, AC algorithms suffer from two drawbacks: the large number of classification rules, and using different pruning methods that may remove vital information to achieve the right decision. In this paper, a new hybrid AC algorithm (HAC) is proposed. HAC applies the power of the Naive Bayes (NB) algorithm to reduce the number of classification rules and to produce several rules that represent each attribute value. Two experiments are conducted on an Arabic textual dataset and the standard Reuters-21578 datasets using six different algorithms, namely J48, NB, classification based on associations (CBA), multi-class classification based on association rules (MCAR), expert multi-class classification based on association rules (EMCAR), and fast associative classification algorithm (FACA). The results of the experiments showed that the HAC approach produced higher classification accuracy than MCAR, CBA, EMCAR, FACA, J48 and NB with gains of 3.95%, 6.58%, 3.48%, 1.18%, 5.37% and 8.05% respectively. Furthermore, on Reuters-21578 datasets, the results indicated that the HAC algorithm has an excellent and stable performance in terms of classification accuracy and F measure.