ムラガキ ヨシヒロ   MURAGAKI Yoshihiro
  村垣 善浩
   所属   研究施設 研究施設
   職種   教授
論文種別 原著
言語種別 英語
査読の有無 査読あり
表題 A Method to Extract Feature Variables Contributed in Nonlinear Machine Learning Prediction
掲載誌名 正式名:Methods of information in medicine
略  称:Methods Inf Med
ISSNコード:00261270/2511705X
掲載区分国外
出版社 Stuttgart : F K Schattauer Verlag
巻・号・頁 Epub ahead of print頁
著者・共著者 SUZUKI Mayumi†, SHIBAHARA Takuma, MURAGAKI Yoshihiro
発行年月 2020/05/07
概要 Background: Although advances in prediction accuracy have been made with new machine learning methods, such as support vector machines and deep neural networks, these methods make nonlinear machine learning models and thus lack the ability to explain the basis of their predictions. Improving their explanatory capabilities would increase the reliability of their predictions.
Objective: Our objective was to develop a factor analysis technique that enables the presentation of the feature variables used in making predictions, even in nonlinear machine learning models.
Methods: A factor analysis technique was consisted of two techniques: backward analysis technique and factor extraction technique. We developed a factor extraction technique extracted feature variables that was obtained from the posterior probability distribution of a machine learning model which was calculated by backward analysis technique.
Results: In evaluation, using gene expression data from prostate tumor patients and healthy subjects, the prediction accuracy of a model of deep neural networks was approximately 5% better than that of a model of support vector machines. Then the rate of concordance between the feature variables extracted in an earlier report using Jensen-Shannon divergence and the ones extracted in this report using backward elimination using Hilbert-Schmidt independence criteria was 40% for the top five variables, 40% for the top 10, and 49% for the top 100.
Conclusion: The results showed that models can be evaluated from different viewpoints by using different factor extraction techniques. In the future, we hope to use this technique to verify the characteristics of features extracted by factor extraction technique, and to perform clinical studies using the genes, we extracted in this experiment.
DOI 10.1055/s-0040-1701615
PMID 32380557