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Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent
Małgorzata Przybyła-Kasperek1, Kwabena Frimpong Marfo1
1Institute of Computer Science, Faculty of Science and Technology, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, Poland.
Entropy (Basel, Switzerland)
|December 24, 2021
Summary
This study introduces a novel classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. The proposed model effectively combines predictions from local data, offering unambiguous decisions superior to traditional fusion methods.
Area of Science:
- Machine Learning
- Data Mining
- Artificial Intelligence
Background:
- Classification challenges with independent, dispersed datasets.
- Limitations of traditional data fusion methods in handling local decision tables.
Purpose of the Study:
- To develop a robust classification model for dispersed data.
- To leverage a neural network for combining predictions from modified k-nearest neighbors algorithm.
Main Methods:
- Modified k-nearest neighbors algorithm for local predictions.
- Multilayer perceptron neural network for fusing prediction results.
- Comparison with fusion methods: majority voting, Borda count, sum rule, decision templates, and theory of evidence.
Main Results:
- Neural network consistently produces unambiguous classification decisions.
- Proposed neural network fusion outperforms other methods when considering only unambiguous results.
- Ambiguous results from other methods can offer multiple decisions for test objects.
Conclusions:
- Neural networks offer a significant advantage in generating clear, unambiguous classifications.
- The proposed model provides a superior approach to data fusion for dispersed datasets.
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