Towards Effective Service Discovery using Feature Selection and Supervised Learning Algorithms

Heyam H Al-Baity,Norah I

doi:10.14569/ijacsa.2019.0100525

Abstract

With the rapid development of web service technologies, the number and variety of web services available on the internet are rapidly increasing. Currently, service registries support human classification, which has been observed to have certain limitations, such as poor query results with low precision and recall rates. With the huge amount of available web services, efficient web service discovery has become a challenging issue. Therefore, to support the effective application of web services, automatic web service classification is required. In recent years, many researchers have approached web service classification problems by applying machine learning methods to automatically classify web services. The ultimate goal of our work is to construct a classifier model that can accurately classify previously unseen web services into the proper categories. This paper presents an intensive investigation on the impact of incorporating feature selection methods (filter and wrapper) on the performance of four state-of-the-art machine learning classifiers. The purpose of employing feature selection is to find a subset of features that maximizes classification accuracy and improves the speed of traditional machine learning classifiers. The effectiveness of the proposed classiﬁcation method has been evaluated through comprehensive experiments on real-world web service datasets. The results demonstrated that our approach outperforms other state-of-the-art methods.

Highlights

The number of the available services that are published on the internet is increasing rapidly
Each classification process for the proposed models consists of four general steps: processing of dataset, applying feature selection to the dataset, classifying web services based on the subset of features obtained from feature selection methods, and selecting the appropriate web service from the classification results
Effective web service classification is a crucial issue for web services

Summary

Introduction

The number of the available services that are published on the internet is increasing rapidly. 1) Support Vector Machine (SVM): SVM is one of the most popular ML algorithms for classification and regression analysis It operates based on the concept of finding a hyperplane that maximizes the margin between two classes [7]. SVM learns from a training dataset, where each data sample is associated with a class label It is effective for problems with large dimensionality, such as image and text categorization, because each data sample in the training set is represented as a point in an n-dimensional space, where n is the number of features. Data samples will be represented in different categories when a large gap divides them. Many hyperplanes can separate a group of data, but the most optimal hyperplane is the hyperplane that creates the largest separation or maximum gap space between classes

Objectives

Results

Conclusion