DQB : a novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles
المؤلف
المصدر
Saudi Journal of Biological Sciences
العدد
المجلد 25، العدد 5 (31 يوليو/تموز 2018)، ص ص. 932-946، 15ص.
الناشر
تاريخ النشر
2018-07-31
دولة النشر
السعودية
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
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 bioinspired 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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Shamlan, Halah M.. 2018. DQB : a novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles. Saudi Journal of Biological Sciences،Vol. 25, no. 5, pp.932-946.
https://search.emarefa.net/detail/BIM-838775
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Shamlan, Halah M.. DQB : a novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles. Saudi Journal of Biological Sciences Vol. 25, no. 5 (Jul. 2018), pp.932-946.
https://search.emarefa.net/detail/BIM-838775
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Shamlan, Halah M.. DQB : a novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles. Saudi Journal of Biological Sciences. 2018. Vol. 25, no. 5, pp.932-946.
https://search.emarefa.net/detail/BIM-838775
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 945-946
رقم السجل
BIM-838775
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر