Web Service classification using Multi-Layer Perceptron optimized with Tabu search

Web Services are emerging technologies enabling machine-machine communication and services reuse over Web. They have an innovative mechanism to render services over diversified environments. Semantic web services are reusable, self-contained software components, to be used independently to fulfil needs or combined with other web services for carrying out complex aggregation through web services composition. There are many factors, due to which methods used for web services composition vary. Service categorization facilitates service retrieval using manual browsing service repositories or through the mechanism of automatic discovery. Classification taxonomies are huge, comprising 1000s categories, at multiple hierarchical levels. Multi-Layer Perceptron Neural Network (MLPNN) is used for classification problems. In this work, Multi-Layer Perceptron optimized with Tabu search (MLP-TS) for learning is proposed. Experimental results demonstrate that the proposed MLP-TS outperforms Multi-Layer Perceptron-Levenberg-Marquardt (MLP-LM) and Multi-Layer Perceptron Back Propagation (MLP-BPP) for web service classification.