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Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran

机译:介绍了用于提高伊朗道路交通事故严重程度分类准确性的聚类分类启发式方法

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摘要

Data-mining algorithms have been employed in many classification problems. In this essay, a number of these algorithms will be used for classification of road accidents severity (casualties or damages). The individual classification algorithms used in this study including Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The above-said classification algorithms have some specific advantages and disadvantages in which features and fields of problems influence on their aptness. During recent years, many studies have been carried out on Ensemble Models in order to achieve better results. In this paper, a hybrid idea of clustering-classification method has been adapted by using k-means and Self-Organizing Maps (SOMs) as clustering methods to improve accuracy of classification. The experiments done on the datasets, show that the pre-clustering can improve the accuracy of classification.
机译:数据挖掘算法已用于许多分类问题。在本文中,将使用这些算法中的许多算法对道路交通事故的严重程度(人员伤亡或破坏)进行分类。本研究中使用的各个分类算法包括人工神经网络(ANN)和自适应神经模糊推理系统(ANFIS)。上述分类算法具有一些特定的优点和缺点,其中问题的特征和领域影响它们的适用性。近年来,为了获得更好的结果,对集成模型进行了许多研究。本文采用k-means和自组织图(SOM)作为聚类方法,采用了聚类分类的混合思想,以提高分类的准确性。在数据集上进行的实验表明,预聚类可以提高分类的准确性。

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