Supervised Classification Box Algorithm Based on Graph Partitioning
| Autor: | Adam Krzyżak, Karima Ben Suliman, Ventzeslav Valev, Nicola Yanev |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Optimization problem
Heuristic (computer science) Computer science Graph partition 02 engineering and technology Computer experiment Computational geometry 01 natural sciences Support vector machine 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing 010306 general physics Algorithm Clique cover |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030197377 CORES |
| DOI: | 10.1007/978-3-030-19738-4_28 |
| Popis: | In this paper we introduce the supervised classification algorithm called Box algorithm based on feature space partitioning. The construction of Box algorithm is closely linked to the solution of computational geometry problem involving heuristic maximal clique cover problem satisfying the k-nearest neighbor rule. We first apply a heuristic algorithm to partition a graph into a minimal number of maximal cliques and subsequently the cliques are merged by means of the k-nearest neighbor rule. The main advantage of the new approach is decomposition of the l-class problem (\(l > 2\)) into l single-class optimization problems. The performance of the Box algorithm is demonstrated to be significantly better than SVM in computer experiments involving real Monk’s dataset from UCI depository and simulated normal data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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