Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

作     者：Demidova, Liliya A. Gorchakov, Artyom, V 

作者机构：MIREA Russian Technol Univ Inst Informat Technol Fed State Budget Educ Inst Higher Educ 78 Vernadsky Ave Moscow 119454 Russia 

出 版 物：《ALGORITHMS》 (算法)

年 卷 期：2022年第15卷第9期

页      面：329-329页

核心收录：

学科分类：0301[法学-法学] 03[法学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：program text classification Markov chains extreme learning machines population-based algorithms biology-inspired algorithms 

摘      要：The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides means for analyzing programs received from students by the end of semester. In this paper, we propose a machine learning-based approach to the classification of student programs represented as Markov chains. The proposed approach enables real-time student submissions analysis in the DTA system. We compare the performance of different multi-class classification algorithms, such as support vector machine (SVM), the k nearest neighbors (KNN) algorithm, random forest (RF), and extreme learning machine (ELM). ELM is a single-hidden layer feedforward network (SLFN) learning scheme that drastically speeds up the SLFN training process. This is achieved by randomly initializing weights of connections among input and hidden neurons, and explicitly computing weights of connections among hidden and output neurons. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones. In addition, we apply biology-inspired algorithms to ELM input weights fine-tuning in order to further improve the generalization capabilities of this algorithm. The obtained results show that ELMs fine-tuned with biology-inspired algorithms achieve the best accuracy on test data in most of the considered problems.