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| A Review of Studies On Machine Learning Techniques.
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(1.56MB) |
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Volume: 1 Issue: 1 |
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Pages: 1-96 |
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Publication
Date: June 2007 |
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ISSN
(Online): 1985-1553 |
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Pages |
70 - 84 |
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Author(s) |
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Published
Date |
30-06-2007 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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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 |
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| 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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| 1 |
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Ian Watson & Farhi Marir. "Case-Based Reasoning: A Review " available at http://www.aicbr. org/classroom/cbr-review.html.
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Pat Langley, Stanford and Herbert A. Simon, Pittsburgh. "Application of Machine Learning and Rule Induction." available at http://cll.stanford.edu/~langley/papers/app.cacm.ps.
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Peter Flach and Nada Lavrac. "Rule Induction" available at www.cs.bris.ac.uk/Teaching/Resources/COMSM0301/materials/RuleInductionSection.pdf.
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Roger J. Lewis. "An Introduction to Classification and Regression Tree (CART) Analysis" Presented at the 2000 Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, California.
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Stephen M Winkler, Michael Aenzeller and Stefan Wagner. "Advances in Applying Genetic Programming to Machine Learning, Focusing on Classification Problems" available at http://www.heuristiclab.com/publications/papers/winkler06c.ps.
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Susanne Hoche. "Active Relational Rule Learning in a Constrained Confidence-Rated Boosting Framework" PhD Thesis, Rheinische Friedrich-Wilhelms-Universitaet Bonn, Germany, December 2004.
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Yisehac Yohannes, Patrick Webb " Classification and Regression Trees" International Food Policy Research Institute, 1999.
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Y. Singh, P. K. Bhatia and O. Sangwan, “Software Reusability Assessment Using Soft Computing Techniques”, Newsletter, ACM SIGSOFT Software Engineering Notes archive, 36 (1), January 2011.
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Y. Singh, A. Kaur, P. K. Bhatia and O. Sangwan, “Predicting Software Development Effort Using Artificial Neural Network”, International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 20(3), pp. 367-375, 2010.
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| Yogesh Singh : Colleagues
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| Pradeep Kumar Bhatia : Colleagues
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| Omprakash Sangwan : Colleagues
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