使用J48树学习方法的增强版AdaBoostM1
Enhanced version of AdaBoostM1 with J48 Tree learning method
摘要：
机器学习专注于构建和研究可以从数据中学习的系统。这与分类问题有关，这通常是机器学习算法的目的。当机器学习方法被在机器学习中没有特殊专长的人使用时，重要的是该方法在分类中是鲁棒的，在某种意义上，以手头问题的最小调整获得合理的性能。算法的评估是基于它们对给定数据进行分类的鲁棒性。在本文中，我们提出了一个可量化的鲁棒性的措施，并描述了一个特定的学习方法，是强大的，根据这一措施的背景下，分类问题。我们提出了自适应Boosting（AdaBoostM 1）与J 48（C4.5树）作为一个基本的学习器与调整权重阈值（P）和迭代次数（I）的boosting算法。为了对性能进行基准测试，我们使用基线分类器AdaBoostM 1和决策树桩作为基础学习者，而无需调整参数。通过调整参数并使用J 48作为基本学习器，我们能够将开发数据集的总体平均错误率比（errorC/errorNB）从2.4降至0.9，并将评估数据集的总体平均错误率比（errorC/errorNB）从2.1降至1.2。
英文摘要：
Machine Learning focuses on the construction and study of systems that can learn from data. This is connected with the classification problem, which usually is what Machine Learning algorithms are designed to solve. When a machine learning method is used by people with no special expertise in machine learning, it is important that the method be robust in classification, in the sense that reasonable performance is obtained with minimal tuning of the problem at hand. Algorithms are evaluated based on how robust they can classify the given data. In this paper, we propose a quantifiable measure of robustness, and describe a particular learning method that is robust according to this measure in the context of classification problem. We proposed Adaptive Boosting (AdaBoostM1) with J48(C4.5 tree) as a base learner with tuning weight threshold (P) and number of iterations (I) for boosting algorithm. To benchmark the performance, we used the baseline classifier, AdaBoostM1 with Decision Stump as base learner without tuning parameters. By tuning parameters and using J48 as base learner, we are able to reduce the overall average error rate ratio (errorC/errorNB) from 2.4 to 0.9 for development sets of data and 2.1 to 1.2 for evaluation sets of data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
