Authors:
Jianshen Zhu
1
;
Kazuya Haraguchi
1
;
Hiroshi Nagamochi
1
and
Tatsuya Akutsu
2
Affiliations:
1
Department of Applied Mathematics and Physics, Kyoto University, Kyoto 606-8501, Japan
;
2
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
Keyword(s):
Machine Learning, Linear Regression, Integer Programming, Linear Program, Cheminformatics, Materials Informatics, QSAR/QSPR, Molecular Design.
Abstract:
In this paper, we propose a new machine learning method, called adjustive linear regression, which can be regarded as an ANN on an architecture with an input layer and an output layer of a single node, wherein an error function is minimized by choosing not only weights of the arcs but also an activation function at each node in the two layers simultaneously. Under some conditions, such a minimization can be formulated as a linear program (LP) and a prediction function with adjustive linear regression is obtained as an optimal solution to the LP. We apply the new machine learning method to a framework of inferring a chemical compound with a desired property. From the results of our computational experiments, we observe that a prediction function constructed by adjustive linear regression for some chemical properties drastically outperforms that by Lasso linear regression.