Binary aggregation functions in software plagiarism detection

© 2017 IEEE. Supervised learning is of key interest in data science. Even though there exist many approaches to solving, among others, classification as well as ordinal and standard regression tasks, most of them output models that do not possess useful formal properties, like nondecreasingness in each independent variable, idempotence, symmetry, etc. This makes them difficult to interpret and analyze. For instance, it might be impossible to determine the importances of individual features or to assess the effects of increasing the values of predictors on the behavior of a chosen response variable. Such properties are especially important in software plagiarism detection, where we are faced with the combination of degrees to which how much a code chunk A is similar to (or contained in) B as well as how much B is similar to A. Therefore, in this paper we consider a new method for fitting B-spline tensor product-based aggregation functions to empirical data. An empirical study indicates a highly competitive performance of the resulting models. Additionally, they possess an intuitive interpretation which is highly desirable for end-users.