The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues

2000 ◽  
Vol 19 (2) ◽  
pp. 117-123 ◽  
Author(s):  
P.N. SubbaNarasimha ◽  
B. Arinze ◽  
M. Anandarajan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Predictive Accuracy ◽  
Data Exploration ◽  
Skewed Data ◽  
Artificial Neural

Combining BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties

2018 ◽  
Vol 11 (2) ◽  
pp. 290-314 ◽  
Author(s):  
Joseph Awoamim Yacim ◽  
Douw Gert Brand Boshoff
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Accuracy ◽  
Back Propagation ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Content Type ◽  
Attribute Weights ◽  
Artificial Neural ◽  
And Training

Purpose The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models. Design/methodology/approach The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics. Findings The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes. Research limitations/implications A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values. Originality/value This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

scholarly journals Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

1970 ◽  
Vol 24 (1) ◽  
pp. 1-14
Author(s):  
Mustafa Özuysal ◽  
Gökmen Tayfur ◽  
Serhan Tanyel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Public Transportation ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Passenger Flow ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

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