A morphological neural network for binary classification problems

• Published in: Engineering applications of artificial intelligence Vol. 65; pp. 12 - 28
• Main Authors: Araújo, Ricardo de A., Oliveira, Adriano L.I., Meira, Silvio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2017
• Subjects: Binary classification; Descending gradient-based learning; Lattice theory; Mathematical morphology; Morphological neural network; Descending gradient-based learning; Lattice theory; Mathematical morphology; Binary classification; Morphological neural network
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2017.07.014

The dilation–erosion perceptron (DEP) is a hybrid morphological processing unit, composed of a balanced combination between dilation and erosion morphological operators, recently presented in the literature to solve some problems. However, a drawback arises from such model for building complex decision surfaces for non-linearly separable data. In this sense, to overcome this drawback, we present a particular class of morphological neural networks with multilayer structure, called the dilation–erosion neural network (DENN), to deal with binary classification problems. Each processing unit of the DENN is composed by a DEP processing unit. Also, a descending gradient-based learning process is presented to train the DENN, according to ideas from Pessoa and Maragos. Furthermore, we conduct an experimental analysis with the DENN using a relevant set of binary classification problems, and the obtained results indicate similar or superior classification performance to those achieved by classical and state of the art models presented in the literature.