Auto-Associative Neural Networks to Improve the Accuracy of Estimation Models

Auto-Associative Neural Networks to Improve the Accuracy of Estimation Models

Salvatore A. Sarcia (Universita di Roma Tor Vergata, Italy), Giovanni Cantone (Universita di Roma Tor Vergata, Italy) and Victor R. Basili (University of Maryland, USA)
DOI: 10.4018/978-1-60566-758-4.ch004
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Abstract

Prediction of software engineering variables with high accuracy is still an open problem. The primary reason for the lack of high accuracy in prediction might be because most models are linear in the parameters and so are not sufficiently flexible and suffer from redundancy. In this chapter, we focus on improving regression models by decreasing their redundancy and increasing their parsimony, i.e., we turn the model into a model with fewer variables than the former. We present an empirical auto-associative neural network-based strategy for model improvement, which implements a reduction technique called Curvilinear component analysis. The contribution of this chapter is to show how multi-layer feedforward neural networks can be a useful and practical mechanism for improving software engineering estimation models.
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Parametric Estimation Models

The estimation models that we refer to are based on parametric models as illustrated in Figure 1.

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