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Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network
المؤلفون المشاركون
Harichandran, Khanna Nehemiah
Elgin Christo, V. R.
Minu, B.
Kannan, A.
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-09-23
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented.
The clinical data are subjected to data preprocessing, feature selection, and classification.
Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation.
Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection.
Each bioinspired algorithm selects a subset of features yielding three feature subsets.
Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets.
The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network.
Ten-fold cross-validation technique has been used to train and test the performance of the classifier.
Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy.
An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset.
The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Elgin Christo, V. R.& Harichandran, Khanna Nehemiah& Minu, B.& Kannan, A.. 2019. Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1130693
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Elgin Christo, V. R.…[et al.]. Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1130693
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Elgin Christo, V. R.& Harichandran, Khanna Nehemiah& Minu, B.& Kannan, A.. Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1130693
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130693
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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