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World competitive contest-based artificial neural network: A new class-specific method for classification of clinical and biological datasets
Genomics ( IF 4.4 ) Pub Date : 2020-09-28 , DOI: 10.1016/j.ygeno.2020.09.047
Zohre Arabi Bulaghi 1 , Ahmad Habibizad Navin 2 , Mehdi Hosseinzadeh 3 , Ali Rezaee 1
Affiliation  

Many data mining methods have been proposed to generate computer-aided diagnostic systems, which may determine diseases in their early stages by categorizing the data into some proper classes. Considering the importance of the existence of a suitable classifier, the present study aims to introduce an efficient approach based on the World Competitive Contests (WCC) algorithm as well as a multi-layer perceptron artificial neural network (ANN). Unlike the previously introduced methods, which each has developed a universal model for all different kinds of data classes, our proposed approach generates a single specific model for each individual class of data. The experimental results show that the proposed method (ANNWCC), which can be applied to both the balanced and unbalanced datasets, yields more than 76% (without applying feature selection methods) and 90% (with applying feature selection methods) of the average five-fold cross-validation accuracy on the 13 clinical and biological datasets. The findings also indicate that under different conditions, our proposed method can produce better results in comparison to some state-of-art meta-heuristic algorithms and methods in terms of various statistical and classification measurements. To classify the clinical and biological data, a multi-layer ANN and the WCC algorithm were combined. It was shown that developing a specific model for each individual class of data may yield better results compared with creating a universal model for all of the existing data classes. Besides, some efficient algorithms proved to be essential to generate acceptable biological results, and the methods' performance was found to be enhanced by fuzzifying or normalizing the biological data.



中文翻译:

基于世界竞争竞赛的人工神经网络:一种用于临床和生物数据集分类的新类别特定方法

已经提出了许多数据挖掘方法来生成计算机辅助诊断系统,这些系统可以通过将数据分类为一些适当的类别来确定疾病的早期阶段。考虑到存在合适分类器的重要性,本研究旨在介绍一种基于世界竞争性竞赛 (WCC) 算法和多层感知器人工神经网络 (ANN) 的有效方法。与之前介绍的方法不同,每个方法都为所有不同类型的数据类开发了一个通用模型,我们提出的方法为每个单独的数据类生成一个单一的特定模型。实验结果表明,所提出的方法(ANNWCC)既适用于平衡数据集,也适用于非平衡数据集,在 13 个临床和生物数据集上产生超过 76%(不应用特征选择方法)和 90%(应用特征选择方法)的平均五倍交叉验证准确度。研究结果还表明,在不同的条件下,与一些最先进的元启发式算法和方法相比,我们提出的方法在各种统计和分类测量方面可以产生更好的结果。为了对临床和生物学数据进行分类,结合了多层 ANN 和 WCC 算法。结果表明,与为所有现有数据类创建通用模型相比,为每个单独的数据类开发特定模型可能会产生更好的结果。此外,一些有效的算法被证明对于产生可接受的生物学结果至关重要,

更新日期:2020-09-28
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