人工智能测试:使用K-means聚类和决策树分析确保数据集(训练和测试)之间的良好数据分离

Kishore Sugali, Christine D. Sprunger, Venkata N. Inukollu
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引用次数: 0

摘要

人工智能和机器学习已经存在很长时间了。近年来,集成人工智能和机器学习技术的应用程序越来越受欢迎。与传统开发一样,软件测试是成功的AI/ML应用程序的关键组成部分。AI/ML中使用的开发方法与传统开发有很大的不同。根据这些区别,出现了各种各样的软件测试挑战。本文的重点是有效地将数据分割成训练数据集和测试数据集的挑战。通过对数据集和决策树应用k-Means聚类策略,我们可以显著增加训练数据集代表完整数据集域的可能性,从而避免训练一个可能失败的模型,因为它只学习了完整数据域的一个子集。
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AI Testing: Ensuring a Good Data Split Between Data Sets (Training and Test) using K-means Clustering and Decision Tree Analysis
Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.
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来源期刊
International Journal of Advances in Soft Computing and its Applications
International Journal of Advances in Soft Computing and its Applications Computer Science-Computer Science Applications
CiteScore
3.30
自引率
0.00%
发文量
31
期刊介绍: The aim of this journal is to provide a lively forum for the communication of original research papers and timely review articles on Advances in Soft Computing and Its Applications. IJASCA will publish only articles of the highest quality. Submissions will be evaluated on their originality and significance. IJASCA invites submissions in all areas of Soft Computing and Its Applications. The scope of the journal includes, but is not limited to: √ Soft Computing Fundamental and Optimization √ Soft Computing for Big Data Era √ GPU Computing for Machine Learning √ Soft Computing Modeling for Perception and Spiritual Intelligence √ Soft Computing and Agents Technology √ Soft Computing in Computer Graphics √ Soft Computing and Pattern Recognition √ Soft Computing in Biomimetic Pattern Recognition √ Data mining for Social Network Data √ Spatial Data Mining & Information Retrieval √ Intelligent Software Agent Systems and Architectures √ Advanced Soft Computing and Multi-Objective Evolutionary Computation √ Perception-Based Intelligent Decision Systems √ Spiritual-Based Intelligent Systems √ Soft Computing in Industry ApplicationsOther issues related to the Advances of Soft Computing in various applications.
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