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A Review of Studies On Machine Learning Techniques.
Yogesh Singh, Pradeep Kumar Bhatia, Omprakash Sangwan
Pages - 70 - 84     |    Revised - 15-06-2007     |    Published - 30-06-2007
Volume - 1   Issue - 1    |    Publication Date - June 2007  Table of Contents
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KEYWORDS
Machine Learning Techniques (MLT), Neural Networks (NN), Case Based Reasoning (CBR), Classification and Regression Trees (CART), Rule Induction, Genetic Algorithms and Genetic Programming
ABSTRACT
This paper provides an extensive review of studies related to expert estimation of software development using Machine-Learning Techniques (MLT). Machine learning in this new era, is demonstrating the promise of producing consistently accurate estimates. Machine learning system effectively “learns” how to estimate from training set of completed projects. The main goal and contribution of the review is to support the research on expert estimation, i.e. to ease other researchers for relevant expert estimation studies using machine-learning techniques. This paper presents the most commonly used machine learning techniques such as neural networks, case based reasoning, classification and regression trees, rule induction, genetic algorithm & genetic programming for expert estimation in the field of software development. In each of our study we found that the results of various machine-learning techniques depends on application areas on which they are applied. Our review of study not only suggests that these techniques are competitive with traditional estimators on one data set, but also illustrate that these methods are sensitive to the data on which they are trained.
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Mr. Yogesh Singh
- India
Mr. Pradeep Kumar Bhatia
- India
Mr. Omprakash Sangwan
- India
sangwan_op@aiit.amity.edu