A Genetic Algorithm for Discovering Classification Rules in Data Mining

Al-Maqaleh, Basheer M.; Shahbazkia, Hamid Reza

doi:10.5120/5644-8072

Cited by 18 publications

(7 citation statements)

References 19 publications

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“…Obviously, both of these genetic-based algorithms have been used for an optimization task rather than a classification one. meanwhile, the algorithms proposed in Al-maqaleh and Shahbazkia (2012) were designed to discover classification rules using a genetic algorithm.…”

Section: Evolutionary Rule-based Classification Modelsmentioning

confidence: 99%

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

Alshamlan

2018

Saudi Journal of Biological Sciences

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In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the Artificial Bee Colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bio-inspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparently (to the best of our knowledge) several new genes were discovered that have not been seen in any past studies. For the applicability demand, based on the results acqured from microarray gene expression analysis, we can conclude that DQB can be adopted in a different real world applications with some modifications.

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Section: Evolutionary Rule-based Classification Modelsmentioning

confidence: 99%

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

Alshamlan

2018

Saudi Journal of Biological Sciences

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“…Genellikle kural çıkarımı yapılan genetik algoritma ile ilgili önceki çalışmalarda kromozom yapısı tek bir kural veya kural listesi şeklinde oluşturulmuş ve böylece başarılı sınıflamalar yapıldığı görülmüştür. Bu çalışmaların bazıları şu şekildedir: veri madenciliğinde genetik algoritma ile sınıflama kuralı keşfi [15], genetik algoritma ile daha anlaşılır sınıflama kuralları keşfi [16], rastgele yapıda olmayan başlangıç popülasyonuna sahip genetik algoritma ile sınıflama kuralı madenciliği [17], genetik algoritma kullanarak örüntü sınıflama [18], dağıtık genetik algoritma kullanarak sınıflama kuralı keşfi [19], genetik algoritma kullanarak sınıflama kuralları oluşturmak için bilgi elde etme aracı [20], veri madenciliğinde kural keşfi için genetik algoritma [21], büyük veri kümelerinde sınıflama kuralı madenciliği için zeki bir genetik algoritma [22], genetik programlama kullanarak çoklu anlaşılır sınıflama kuralı madenciliği [23]. Fakat ateşböceği algoritmasının genetik algoritmaya göre daha yeni ve daha basit uygulanabilir olması ayrıca bazı optimizasyon problemlerinde daha iyi sonuçlar elde edildiğinin [24] gösterilmesi üzerine kural çıkarım süreci bu çalışmada iyileştirilmiş ateşböceği algoritması ile yapılmıştır.…”

Section: Li̇teratür öZeti̇ Ve İlgi̇li̇ çAlişmalar (Literature Review and unclassified

Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması

Aydilek¹

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…A Genetic algorithm based approach was proposed by B.M. Al-Maqaleh [5] for mining classification rules from large data base and those rules discovered by the algorithm have higher classification performance to unknown data.…”

Section: Single Approachesmentioning

confidence: 99%

Analysis of Time Series Rule Extraction Techniques

Suresh¹

2013

IOSR-JCE

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In Data mining, the sequence of data points are measured typically at successive time instants spaced at uniform time intervals are called time series. The real applications of time series are frequent pattern analysis, bioinformatics, medical treatment, meteorology, sociology and economics. Frequent patterns can be analyzed to give explanatory rules and this rule extraction can be done using many algorithms like Genetic Algorithm, Fuzzy Logic , Support Vector Machine etc. Rule induction is an area of machine learning in which rules are extracted from collective set of observations. The rules extracted may represent complete scientific model of the data, or simply represent local patterns in the data. A brief overview of some of the most common rule extraction techniques and a comparison between single and hybrid rule approaches comprise in this survey.

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A Genetic Algorithm for Discovering Classification Rules in Data Mining

Cited by 18 publications

References 19 publications

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması

Analysis of Time Series Rule Extraction Techniques

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