A class of hybrid multilayer perceptrons for software development effort estimation problems. (30th December 2017)

• Record Type: Journal Article
• Authors: de A. Araújo, Ricardo; Oliveira, Adriano L.I.; Meira, Silvio
• Is Part Of: Expert systems with applications. Volume 90(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-12-30
• Subjects: Hybrid multilayer perceptron -- Mathematical morphology -- Descending gradient -- Software development -- Effort estimation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.07.050
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A class of multilayer neural network with hybrid processing units. A gradient-based learning process to design the proposed model. An experimental analysis using relevant datasets of software development effort estimation. Abstract: The dilation-erosion-linear perceptron is a hybrid morphological neuron which has been recently proposed in the literature to solve some prediction problems. However, a drawback arises from such model for building mappings to solve tasks with complex input-output nonlinear relationships within effort estimation problems. In this sense, to overcome this limitation, we present a particular class of hybrid multilayer perceptrons, called the multilayer dilation-erosion-linear perceptron (MDELP), to deal with software development effort estimation problems. Each processing unit of the proposed model is composed of a mix between a hybrid morphological operator (given by a balanced combination between dilation and erosion operators) and a linear operator. According to Pessoa and Maragos's ideas, we propose a descending gradient-based learning process to train the proposed model. Besides, we conduct an experimental analysis using relevant datasets of software development effort estimation and the achieved results are discussed and compared, according to MMRE and PRED25 measures, to those obtained by classical and state of the art models presented in the literature.