Paper Info
Title
Adaptive one-pass passive-aggressive radial basis function for classification problems
Abstract
This paper presents a novel adaptive one-pass Passive-Aggressive Radial Basis Function (APARBF) for classification problems. The APARBF uses elliptic Gaussian neurons followed by a Softmax layer and cross-entropy loss function. This network tries to overcome the elasticity-plasticity dilemma by using the Passive-Aggressive (PA) algorithm and adapting the hidden layer structure. The weight updates have to be plastic to acquire the most information from each sample and at the same time need to be elastic to retrain the information from the past instances. Inspired by PA, a novel update formula for cross-entropy loss minimization has been derived. The adaptive design of the network lets it start with zero hidden neurons and grow or shrink according to the data. For kernel parameters, the adaptive structure determines the correct number of hidden neurons and updates recursively each neuron’s center and covariance matrix. To evaluate our network, we perform two series of experiments. The first experiments compare the proposed APARBF with other recently developed one-pass algorithms (i.e., OVIG, OBHT, SCW, AROW, OGD, and PA). The subsequent experiments include comparing the proposed algorithm with some online adaptive structures such as FGAP-RBF, C-Mantec, McNN, and PBL-McNN, based on their mean classification error and the number of hidden neurons. Wilcoxon sign rank test and Friedman test clearly show the superiority of the proposed network’s results compared with its competitors in one-pass classification problems.
Year
DOI
Venue
2022
10.1016/j.neucom.2022.03.047
Neurocomputing
Keywords
DocType
Volume
One-pass algorithms,Adaptive structure neural network,Passive-aggressive algorithm,Radial basis function,Cross-entropy
Journal
491
ISSN
Citations 
PageRank 
0925-2312
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Maedeh Kafiyan-Safari100.34
Modjtaba Rouhani200.34