Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case

Frontiers in Psychology

Jocelyn Holden Bolin¹ 

关键词: supervised classification;    training data;    misclassification;    classification and regression trees;    random forests;    discriminant analysis;   

DOI  :  10.3389/fpsyg.2014.00118

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

【 授权许可】

CC BY