Open AccessRanking methods for inputs of functionally-mathematical libraries with inaccessible source code
Author(s) -
Ya.B. Komarov,
AUTHOR_ID,
A.A. Sherminskaya,
Alexey Nikolaev,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
trudy krylovskogo gosudarstvennogo naučnogo centra
Language(s) - English
Resource type - Journals
eISSN - 2618-8244
pISSN - 2542-2324
DOI - 10.24937/2542-2324-2021-2-s-i-91-96
Subject(s) - executable , ranking (information retrieval) , computer science , artificial neural network , rank (graph theory) , code (set theory) , source code , set (abstract data type) , quality (philosophy) , artificial intelligence , machine learning , algorithm , data mining , mathematics , programming language , philosophy , epistemology , combinatorics
This work mainly aimed to identify the most suitable calculation approaches and methods for the effect of inputs upon the outputs of a functionally-mathematical library with inaccessible source code, and further use these methods and approaches to develop an input ranking tool. These methods and approaches were studied using a generalized regressive model representing an arbitrary functionally-mathematical library (an executable module). The paper studied two approaches to the determination of input effects upon the outputs: 1) correlation analysis; 2) neural network method. The first method is analytical calculation (couple by couple) of Pierson correlation coefficients for all input and output parameters. As an alternative, the study also investigated rank-based Spierman and Kendall correlations. The second approach basically meant neural network learning with required accuracy using the variation set of executable module inputs and their respective outputs with subsequent calculation of contribution from each parameter to the overall result for the neural network after learning. These approaches have also been compared in terms of several major criteria, i.e. application peculiarities, speed and output quality. The analysis has shown that neural-network learning method, despite its certain drawbacks, is more suitable for the task of this study. The paper also outlined possible ways for further improvement of this method and complexity increase of the suggested functionally-mathematical library model.