Mining Logical Arithmetic Expressions From Proper Representations
Eitan Kosman, Technion, Israel Institute of Technology, Haifa, Israel; Ilya Kolchinsky, Red Hat and Technion, Israel Institute of Technology, Haifa, Israel; and Assaf Schuster, Technion, Israel Institute of Technology, Haifa, Israel
Logical-arithmetic expressions are convenient for describing phenomena due to their expressiveness and comprehensibility. Therefore, we propose to target mining logical arithmetic expressions through a novel task called Logical-arithmetic expression mining (LAEM). Its goal is to discover expressive logical expressions that are representative for a database. It accepts a complex database as input and returns a set of representative expressions for the database. Driven by the success of machine learning models to recognize complex patterns, we argue that a thorough modeling of the learned representations could be exploited for generating interesting and representative mathematical expressions. To address this, in this paper we propose Soft dEcision Tree for logical arithmetic Expressions miNing (SEEN), an algorithm based on representation learning for generating logical expressions. Our mining mechanism partitions the learned representation space and assigns self-labels. Then, we use the self-labels to train a multivariate soft decision tree from which we generate logical arithmetic expressions. A comprehensive experimental study on 2 diverse real-world datasets shows that the proposed method is able to generate interesting expressions. The implementation is publicly available1.