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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Collaborative rule generation: An ensemble learning approach
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Collaborative rule generation: An ensemble learning approach

机译：协作规则生成：整体学习方法

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Due to the vast and rapid increase in data, data mining has become an increasingly important tool for the purpose of knowledge discovery in order to prevent the presence of rich data but poor knowledge. Data mining tasks can be undertaken in two ways, namely, manual walkthrough of data and use of machine learning approaches. Due to the presence of big data, machine learning has thus become a powerful tool to do data mining in intelligent ways. A popular approach of machine learning is inductive learning, which can be used to generate a rule set (a set of rules) using a particular algorithm. Inductive learning can involve a single base algorithm learning from a single data set following a standard learning approach. In this approach, the learning algorithm can generate a single rule set such as decision trees. On the other hand, the inductive learning can also involve a single base algorithm learning from multiple data sets following an ensemble learning approach. In this approach, the learning algorithm can generate multiple rule sets such as random forests. The latter approach is usually designed to reduce overfitting of models that usually arises when the former approach is adopted. In this context, the ensemble learning approach usually enables the improvement of the overall accuracy in prediction. The aim of this paper is to introduce a new approach of ensemble learning called Collaborative Rule Generation. In the new approach, the inductive learning involves multiple base algorithms learning from a single data set to generate a single rule set, which aims to enable each rule to have a higher quality. This paper also includes an experimental study validating the Collaborative Rule Generation approach and discusses the results in both quantitative and qualitative ways.
机译：由于数据的巨大且快速的增长，数据挖掘已成为用于知识发现目的的越来越重要的工具，以防止存在丰富的数据但知识贫乏。数据挖掘任务可以通过两种方式来完成，即数据的手动遍历和使用机器学习方法。由于存在大数据，因此机器学习已成为以智能方式进行数据挖掘的强大工具。机器学习的一种流行方法是归纳学习，它可以用于使用特定算法生成规则集（一组规则）。归纳学习可以包括遵循标准学习方法从单个数据集进行单个基础算法学习。在这种方法中，学习算法可以生成单个规则集，例如决策树。另一方面，归纳学习还可以遵循集成学习方法，从多个数据集中学习单个基础算法。在这种方法中，学习算法可以生成多个规则集，例如随机森林。后一种方法通常旨在减少采用前一种方法时通常出现的模型的过度拟合。在这种情况下，集成学习方法通​​常可以提高预测的整体准确性。本文的目的是介绍一种称为协作规则生成的集成学习新方法。在新方法中，归纳学习涉及从单个数据集学习多个基本算法，以生成单个规则集，其目的是使每个规则具有更高的质量。本文还包括验证协作规则生成方法的实验研究，并以定量和定性的方式讨论了结果。

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