Abstract

The expressivity of machine learning algorithms is considered to be critical in intelligent data analysis tasks for practical application. As an alternative set of classification rule learning algorithms to conventional decision tree, Prism family of algorithms induce modular rules concisely, thus exhibiting good expressiveness for human users. However, existing Prism rule induction techniques are limited by the assumption of Gaussian distribution for quantitative attributes, and may not be available for real life data analyzing, in which skewness is commonly observed. For this reason, we investigate a skew-adaptive mechanism for rule term boundary delimitation in Prism inductive learning. The propose algorithm, called P2-Prism, could learn expressive classification rules directly from quantitative data beyond Gaussian distribution. By employing statistical inference characteristics of Poisson process, our mechanism provides a significant contribution to classification rule inductive learning with adaption of skewed data distribution. The experimental evaluation of our algorithm demonstrates its skew-adaptive superiority on benchmark datasets, comparing with state-of-the-art algorithms. Furthermore, it is shown that P2-Prism is a robust classifier in the presence of various levels of noise, which further reveals its adaptability to the skewness of data distribution.

中文翻译：

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利用偏斜定界机制学习表达分类规则

摘要

在实际应用的智能数据分析任务中，机器学习算法的表达被认为至关重要。作为传统决策树的另一套分类规则学习算法，Prism算法系列简洁明了地引入了模块化规则，因此对人类用户具有良好的表现力。然而，现有的棱镜规则归纳技术受高斯分布的定量属性假设的限制，并且可能不适用于通常观察到偏斜的现实数据分析。因此，我们研究了棱镜归纳学习中规则项边界定界的偏斜适应机制。提出的算法称为P2-Prism，可以直接从超出高斯分布的定量数据中学习表达分类规则。通过利用泊松过程的统计推断特征，我们的机制为适应倾斜数据分布的分类规则归纳学习做出了重要贡献。与最新算法相比，我们算法的实验评估证明了其在基准数据集上的偏斜适应优势。此外，表明P2-Prism是存在各种噪声水平时的鲁棒分类器，这进一步揭示了其对数据分布偏斜的适应性。与最新算法进行比较。此外，表明P2-Prism是存在各种噪声水平时的鲁棒分类器，这进一步揭示了其对数据分布偏斜的适应性。与最新算法进行比较。此外，表明P2-Prism是存在各种噪声水平时的鲁棒分类器，这进一步揭示了其对数据分布偏斜的适应性。