特征提取方法在软件成本估算中的评价

First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007) Pub Date : 2007-09-20 DOI:10.1109/ESEM.2007.57

Burak Turhan, Onur Kutlubay, A. Bener

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引用次数: 12

摘要

本文研究了线性和非线性特征提取方法对成本估计性能的影响。我们使用主成分分析(PCA)和Isomap从观察到的特征中提取新特征，并在公开可用的数据集上使用支持向量回归(SVR)对这些方法进行评估。我们对这些数据集的结果表明，这些线性和非线性特征提取方法的性能没有显著差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Evaluation of Feature Extraction Methods on Software Cost Estimation

This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use principal component analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods with support vector regression (SVR) on publicly available datasets. Our results for these datasets indicate there is no significant difference between the performances of these linear and non-linear feature extraction methods.

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First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)

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