Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 32-49 ◽  
Author(s):  
Sanjeev Kumar Dash ◽  
Aditya Prakash Dash ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Information Gain ◽  
Radial Basis Function Network ◽  
Network Efficiency ◽  
Novel Approach ◽  
Radial Basis ◽  
Unbalanced Dataset

This work presents a novel approach for classification of both balanced and unbalanced dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact it size. In this paper, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

2018 ◽  
pp. 218-243
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak
Keyword(s):  
Feature Selection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Missing Values ◽  
Information Gain ◽  
Noise Removal ◽  
Network Efficiency ◽  
Novel Approach ◽  
Radial Basis ◽  
Filter Approach

This chapter presents a novel approach for classification of dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact its size. In this chapter, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. Different feature selection methods, handling missing values and removal of inconsistency to improve the classification accuracy of the proposed model are emphasized. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

scholarly journals Coking energy consumption radial basis function prediction model improved by differential evolution algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1122-1130 ◽  
Author(s):  
Wenhua Tao ◽  
Jiao Chen ◽  
Yunjin Gui ◽  
Pingping Kong
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Differential Evolution Algorithm ◽  
Function Prediction ◽  
Radial Basis ◽  
Evolution Algorithm

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.

Discriminating Input Variables for Fraud Detection using Radial Basis Function Network

2018 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
I. O. Alabi ◽  
R. G. Jimoh
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Information Gain ◽  
Radial Basis Function Network ◽  
Data Gathering ◽  
Fraud Detection ◽  
Fixed Time ◽  
Rbf Network ◽  
Radial Basis ◽  
Input Variables

Fraud is an adaptive crime; special methods of data gathering and analysis are required to combat fraud issues as criminals often quest for dubious techniques to evade detection. Radial basis function (RBF) network, was used to build base models that identifies and detect the risk of fraud in transactions. At first, it is imperative to isolate the basic factors that are predictive of fraud occurrences so as to determine the Information gain of each attribute. The input variables’ importance was ascertained to indicate how some of the input variables were distinguished as strong indicators or weak indicators of fraud. Hence, the relevant attributes were selected prior to examining the model’s performance. This study has found relevance among corporate business professionals and government agencies, to minimizing the time and cost of fraud detection. The researcher recommended that fraud mining processes be regularly updated at fixed time intervals to checkmate criminals.

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