Automatically mining relevant variable interactions via sparse Bayesian learning

Ryoichiro Yafune, Daisuke Sakuma, Mirai Takayanagi, Yasuo Tabei, Noritaka Saito, Hiroto Saigo

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the rapid increase in the availability of large amount of data, prediction is becoming increasingly popular, and has widespread through our daily life. However, powerful nonlinear prediction methods such as deep learning and SVM suffer from interpretability problem, making it hard to use in domains where the reason for decision making is required. In this paper, we develop an interpretable non-linear model called itemset Sparse Bayes (iSB), which builds a Bayesian probabilistic model, while simultaneously considering variable interactions. In order to suppress the resulting large number of variables, sparsity is imposed on regression weights by a sparsity inducing prior. As a subroutine to search for variable interactions, itemset enumeration algorithm is employed with a novel bounding condition. In computational experiments using real-world dataset, the proposed method performed better than decision tree by 10% in terms of r2. We also demonstrated the advantage of our method in Bayesian optimization setting, in which the proposed approach could successfully find the maximum of an unknown function while maintaining transparency. Apart from Bayesian optimization with Gaussian process, iSB gives us a clue to understand which variables interactions are important in optimizing an unknown function.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728188089
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Automatically mining relevant variable interactions via sparse Bayesian learning'. Together they form a unique fingerprint.

Cite this