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

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 b...

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Veröffentlicht in:Algorithms Jg. 15; H. 9; S. 329
Hauptverfasser: Demidova, Liliya A., Gorchakov, Artyom V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2022
Schlagworte:
Algorithms
Artificial neural networks
Automation
Biology
biology-inspired algorithms
Cheating
Colleges & universities
Datasets
Differential thermal analysis
extreme learning machines
K-nearest neighbors algorithm
Machine learning
Markov chains
Markov processes
Neural networks
Neurons
population-based algorithms
program text classification
Students
Support vector machines
ISSN:1999-4893, 1999-4893
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Zusammenfassung: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.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a15090329