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Data Classification Using Machine Learning Approach

机译:使用机器学习方法进行数据分类

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摘要

Currently, Internet has numerous effects on our everyday lifecycle. Its significance as an intermediate for commercial transactions will develop exponentially throughout the next years. In terms of the engaged marketplace volume, the Business to Business region will hereby be the supreme exciting area. As the extensive usage of electronic business transactions increase, great volume of products information gets generated and managing such large information automatically becomes a challenging task. The accurate classification of such products to each of the existing classes also becomes an additional multifarious task. The catalog classification is an essential part for operative electronic business applications and classical machine learning problems. This paper presents a supervised Multinomial Na?ve Bayes Classifier machine learning algorithm to classify product listings to anonymous marketplaces. If the existing products are classified under the master taxonomy, the task is to automatically categorize a new product into one of the existing categories. Our algorithm approach proposes a method to accurately classify the existing millions of products
机译:目前,互联网对我们的日常生活克利有很多影响。其作为商业交易中级的重要性将在明年年内呈指数级。在订阅的市场卷方面,向商业区的企业将成为最高令人兴奋的地区。随着电子商务交易的广泛使用,增加了大量的产品信息,并管理此类大型信息自动成为一个具有挑战性的任务。对每个现有类别的这种产品的准确分类也成为一个额外的多种任务。目录分类是操作电子商务应用和古典机器学习问题的重要组成部分。本文介绍了一个监督的多项式Na ve Bayes分类器机器学习算法,将产品列表对匿名市场进行分类。如果现有产品在主分类系统下分类,则任务是自动将新产品分类为现有类别之一。我们的算法方法提出了一种准确地分类现有数百万产品的方法

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