F
Fadi Thabtah
Researcher at Manukau Institute of Technology
Publications - 129
Citations - 5097
Fadi Thabtah is an academic researcher from Manukau Institute of Technology. The author has contributed to research in topics: Association rule learning & Statistical classification. The author has an hindex of 30, co-authored 115 publications receiving 3736 citations. Previous affiliations of Fadi Thabtah include University of Huddersfield & Melbourne Polytechnic.
Papers
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Phishing detection based Associative Classification data mining
TL;DR: Experimental results show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms and generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance.
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Data imbalance in classification: Experimental evaluation
TL;DR: The goal of this paper is to demonstrate the effects of class imbalance on classification models and determine that the relationship between the class imbalance ratio and the accuracy is convex.
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A review of associative classification mining
TL;DR: This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria.
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Predicting phishing websites based on self-structuring neural network
TL;DR: An intelligent model for predicting phishing attacks based on artificial neural network particularly self-structuring neural networks is proposed that shows high acceptance for noisy data, fault tolerance and high prediction accuracy.
Proceedings ArticleDOI
MMAC: a new multi-class, multi-label associative classification approach
TL;DR: Results for 28 different datasets show that the MMAC approach is an accurate and effective classification technique, highly competitive and scalable in comparison with other classification approaches.