Research on Data System Data Classification Methods Based on Convolutional Neural Networks

L. Yu, Xiaoxin Ru
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Abstract

A large amount of business data contains a huge value in the information system. Especially since the information system has entered various industries, the industry knowledge contained in it often helps promote the development of the industry. Therefore, data system data mining is very important. However, there are a large amount of non-balanced data inside the information system, which is difficult to apply for traditional classifiers. This article proposes to use integrated learning ideas to solve a small amount of data in the non-balanced data classification of non-balanced data classification through deep convolutional neural networks, which is easily ignored. It is expected that this article's research can help the information system data classification work.
基于卷积神经网络的数据系统数据分类方法研究
在信息系统中,大量的业务数据蕴含着巨大的价值。尤其是信息系统进入各行各业以后,其中所蕴含的行业知识往往有助于推动行业的发展。因此,数据系统的数据挖掘是非常重要的。然而,信息系统内部存在大量的非平衡数据,传统的分类器很难应用这些数据。期望本文的研究能对信息系统的数据分类工作有所帮助。
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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