Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 549390
Title Performance tuning for machine learning-based software development effort prediction models
Author(s) Ertugrul, Egement; Baytar, Zakir; Catal, C.; Muratli, Can
Source Turkish Journal of Electrical Engineering & Computer Sciences 27 (2019). - ISSN 1300-0632 - p. 1308 - 1324.
Department(s) Information Technology
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and
parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (Grid-
Search), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn
package in the Python language. The proposed model uses a multilayer perceptron as its underlying algorithm, applies binning of the features to transform continuous features and one-hot-encoding technique to transform categorical data into numerical values as feature transformation techniques, does feature selection based on the principal component analysis method, and performs parameter tuning based on the GridSearch algorithm. We demonstrate that our effort prediction model mostly outperforms the other existing models in terms of prediction accuracy based on the mean absolute residual parameter.
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