A New Neural Dynamic Classification Algorithm

Abstract

The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal of: 1) discovering the most effective feature spaces and 2) finding the optimum number of features required for accurate classification using the patented robust neural dynamic optimization model of Adeli and Park. The new classification algorithm is compared with the probabilistic neural network (PNN), enhanced PNN (EPNN), and support vector machine using two sets of classification problems. The first set consists of five standard benchmark problems. The second set is a large benchmark problem called Mixed National Institute of Standards and Technology database of handwritten digits. In general, NDC yields the most accurate classification results followed by EPNN. A beauty of the new algorithm is the smoothness of convergence curves which is an indication of robustness and good performance of the algorithm. The main aim is to maximize the prediction accuracy.

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…The large number of published articles on MI–BCI tasks using EEG signals highlights the importance of the applicability of EEG signals in the BCI domain (Hwang et al, ; Ortiz‐Rosario and Adeli, ). Most EEG‐based BCI systems usually use a structured approach that includes three main steps: (a) preprocessing that may contain three suboperations of noise removal (NR; Çınar, S., and Acır, ; Mutanen et al, ), channel selection (CS; Ghaemi et al, ; Rathee et al, ), and data augmentation (Kalunga et al, 2015; Krell et al, 2018); (b) feature construction, that is choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, ; Zhang et al, ; Aghaei et al, ; Cai et al, ), and feature selection (Zhang et al, ; Lin et al, ; Ma et al, ); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, ; Dai and Cao, ; Direito et al, ), probabilistic neural networks (Adeli and Panakkat, ; Sankari and Adeli, ), enhanced probabilistic neural network (Ahmadlou and Adeli, ; Hirschauer et al, ; Fernandes et al, ), competitive probabilistic neural network (Zeinali and Story, ), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, ), or a combination or ensemble of classifiers (Oliveira‐Santos et al ; Reyes et al ). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

“…choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, 2015;Zhang et al, 2015;Aghaei et al, 2016;Cai et al, 2017), and feature selection Lin et al, 2017;Ma et al, 2018); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, 2017;Dai and Cao, 2017;Direito et al, 2017), probabilistic neural networks (Adeli and Panakkat, 2009;Sankari and Adeli, 2011), enhanced probabilistic neural network (Ahmadlou and Adeli, 2010;Hirschauer et al, 2015;Fernandes et al, 2016), competitive probabilistic neural network (Zeinali and Story, 2017), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, 2017), or a combination or ensemble of classifiers (Oliveira-Santos et al 2018;Reyes et al 2018). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

“…In addition, because the developed adaptive local approximation method in this paper is based on the characteristics of SVM, such as the support hyperplanes, other classification algorithms cannot be directly employed to take place of SVM. Some other popular classification algorithms have been extensively used in practical engineering, such as neural network (Ahmadlou & Adeli, ; Koziarski & Cyganek, ; Molina‐Cabello, Luque‐Baena, López‐Rubio, & Thurnhofer‐Hemsi, ; Wang & Bai, ; Xue & Li, ), neural dynamic classification (Rafiei & Adeli, , ), and deep learning techniques (Gao & Mosalam, ; Hashemi & Abdelghany, ; Rafiei & Adeli, , ; Rafiei, Khushefati, Demirboga, & Adeli, ; Zhang et al., ; Ortega‐Zamorano, Jerez, Gómez, & Franco, ; Torres, Galicia, Troncoso, & Martínez‐Álvarez, ). The applications of these classification algorithms in SRA‐RI can be investigated.…”

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

“…To enhance further the framework, the proposed deep learning algorithm can be fused with other algorithms. For example, the novel neural dynamic classification algorithm developed by Rafiei and Adeli (2017) and validated using convergence curves; and noise models developed by Koziarski and Cyganek (2017) show that there is great promise in further enhancing the computational model developed in this paper. Furthermore, after more granular input controls are implemented in the simulation, real-world data (real driving data on highways, local roads, etc.)…”