Machine learning-based processing of unbalanced data sets for computer algorithms

IF 1.1 Q3 COMPUTER SCIENCE, THEORY & METHODS
Qingwei Zhou, Yongjun Qi, Hailing Tang, Peng Wu
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引用次数: 0

Abstract

Abstract The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.
基于机器学习的不平衡数据集处理的计算机算法
科技的飞速发展使人们获得了大量的数据,这些数据中包含着重要的信息和各种各样的噪声。如何从数据中获取有用的知识是机器学习这个阶段最重要的事情。不平衡分类问题是当前数据挖掘和机器学习领域的一个重要课题,目前该问题越来越受到关注,是学术界和工业界面临的一个比较新的挑战。不平衡分类问题涉及在数据不足或类别分布偏差严重的情况下对数据进行分类。由于不平衡数据集固有的复杂性,需要更多新的算法和工具将大量的原始数据有效地转化为有用的信息和知识。不平衡数据集是分类问题的一种特殊情况,类之间的分布是不均匀的,很难对数据进行准确的分类。本文主要介绍了基于ML的非平衡数据集处理方法的计算机算法处理方法的研究,旨在为基于ML的非平衡数据集计算机算法的处理提供一些思路和方向。本文提出了基于ML的非平衡数据集处理的研究策略,包括数据预处理、决策树数据分类算法、C4.5算法等。进行了基于ML的非平衡数据集处理方法的研究实验。本文的实验结果表明,基于ML的决策树C4.5算法准确率为94.80%,可以更好地用于基于ML的非平衡数据集处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Open Computer Science
Open Computer Science COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
4.00
自引率
0.00%
发文量
24
审稿时长
25 weeks
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