从心理学数据中发现可理解的模式

近年来,机器学习技术常被用于分析心理学数据,以期从数据中找出有价值的模式,更好地刻画和调整人们的心理行为。提出采用二次学习风范的规则生成算法,结合规则学习算法的在模式理解性方面的优势和集成学习、支持向量机等高性能算法在泛化性能上的优势,从心理学数据中发现准确且易于理解的模式。实验表明,采用二次学习风范的规则生成算法在泛化性能上显著高于传统的规则生成算法,且在许多情况下,其输出规则的可理解性亦优于传统的规则生成算法。

Finding Comprehensible Patterns from Psychology Data

In recent years, machine learning techniques have been used to analyze psychology data, with the hope of finding comprehensible patterns to depict and adjust psychological behaviors of human beings. This paper proposes twice-learning style rule generation algorithms that combine the comprehensibility of rule-based learning methods and generalization performance of state-of-the-art learning methods like ensembles and support vector machines (SVMs). Experimental results show that outputs produced by twice-learning style algorithms are always much more accurate and in many cases are more comprehensible than those produced by traditional rule induction algorithms.