元学习的不确定性特征构建及初步分析

元学习即应用机器学习的方法(元算法)寻求问题的特征(元特征)与算法相对性能测度间的映射,从而形成元知识的学习过程,如何构建和提取元特征是其重要的研究内容.针对目前相关研究所用到的元特征大部分是数据的统计特征的问题,提出不确定性建模并研究不确定性对于学习系统的影响.根据样本的不一致性、边界的复杂性、模型输出的不确定性、线...

Feature construction and preliminary analysis of uncertainty for meta-learning

Meta-learning is the learning process of applying machine learning methods （meta-algorithms） to seek the mapping between features of a problem （meta-features） and relative performance measures of the algorithm， thereby forming the learning process of meta-knowledge. How to construct and extract meta-features is an important research content. Concerning the problem that most of meta-features used in the existing related researches are statistical features of data， uncertainty modeling was proposed and the impact of uncertainty on learning system was studied. Based on inconsistency of data， complexity of boundary， uncertainty of model output， linear capability to be classified， degree of attribute overlap， and uncertainty of feature space， six kinds of uncertainty meta-features were established for data or models. At the same time，the uncertainty size of the learning problem itself was measured from different perspectives， and specific definitions were given. The correlations between these meta-features were analyzed on artificial datasets and real datasets of a large number of classification problems， and multiple classification algorithms such as K-Nearest Neighbor （KNN） were used to conduct a preliminary analysis of the correlation between meta-features and test accuracy. Results show that the average degree of correlation is about 0.8， indicating that these meta-features have a significant impact on learning performance.