构建高级密集分类器

Paul-Stefan Popescu, M. Mihăescu, M. Mocanu, D. Burdescu
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引用次数: 4

摘要

在各种软件系统提供的数据源上对项目(即学生、基因等)进行分类是一项重要的任务,由于与系统的业务目标相关的许多原因,需要执行该任务。本文提出了数据分析系统设计者需要考虑的几种方法,他们的目标是获得实现几个额外功能的高级分类器。本文提出的引擎在从Tesys在线教育环境中获得的真实数据上进行了初步测试,试图为现有学生确定最合适的导师。我们的目标是构建一个容纳数据的决策树分类器。这个新的数据结构扩展了决策树的功能,称为DenseJ48。除了处理数据时可能使用的核心功能外,这个新的分类器还有效地实现了几个额外的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building an advanced dense classifier
Classification of items (i.e. students, genes, etc.) on data sources provided by various software systems represents an important task that needs to be performed for many reasons related to the business goals of the system. This paper presents several approaches that need to be taken into consideration by a data analysis system designer who aims to obtain an advanced classifier that implements several extra functionalities. The engine presented in this paper is preliminary tested on real data obtained from Tesys on-line educational environment in an attempt to determine the most suitable tutors for currently existing students. Our goal is to build a Decision Tree classifier that accommodates data. This new data structure extends the functionality of a Decision Tree and is called DenseJ48. This new classifier implements efficiently several extra functionalities besides the core ones that may be used when dealing with data.
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