COMPARISON OF THE PREDICTION ACCURACY THRU ARTIFICIAL NEURAL NETWORKS WITH RESPECT TO MULTIPLE LINEAR REGRESSION USING R

Journal Title: Engineering and Technology Journal - Year 2019, Vol 4, Issue 7

Abstract

In this paper a comparison of the prediction accuracy of a response variable given a set of predictors was made using statistical and artificial intelligence methods using R language. The compared methods were the multiple lineal regression by the least squares method and the backpropagation network (BP). The goal was to decrease the reducible error when predicting the output variable and being able to select a model, an indispensable step when developing a prediction model. The methodology consisted in two validation strategies. The first strategy measured just the training error rate using 100% of data. The second strategy used a validation set approach, dividing the observations in two parts, 50% is for a training set used to fit the models, and the remaining 50% is for a validation set used to test the fitted models. This methodology made it possible a comparison between the training error rate and testing error rate. The measures utilized to evaluate the efficiency were the sum of squared error (SSE) and the coefficient of determination (R2). The results showed that BP network can significantly decrease the reducible error improving the prediction accuracy. It is important to highlight the prediction accuracy with new or unseen observations not used during the training instead of how well the models work with the training data.

Authors and Affiliations

Carlos Eduardo Belman López,   José Alfredo Jiménez García,   José Antonio Vázquez López 

Keywords

Related Articles

Fly Ash Utilization Analysis as A Substitute of Cement in Cement Treated Base (CTB)

Cement Treated Base (CTB) is a pavement layer located between the sub-base and surface layers. This pavement layer uses fine aggregate (sand) and cement as a binder. Fly ash is coal burning waste that can be used as an a...

Solar Powered Sound Automated Male Pee Toilet System for Protection against Coronavirus

The pandemic and emergence of Coronavirus that have destroyed the world economies unexpectedly, broken health-care systems of many nations and emptied many public spaces, separated many families, friends and many people...

WIND FARM LAYOUT OPTIMIZATION USING GENETIC ALGORITHMS AND DESIGN OF EXPERIMENTS

Wind power has become the renewable energy with more participation in countries looking for environmental sustainability. Wind power is transformed into electric power by means of wind turbines, which are generally group...

STUDY OF THE CHOICE OF FREIGHT TRANSPORTATION MODES BETWEEN TRUCKS AND RIVER TRANSPORTATION ON BANJARMASIN – MUARA TEWEH ROUTE

Transportation is a fundamental factor in the life of a nation and state, has a function as a driver and support for development. Users of transportation services are the wider community who need transportation services....

Knowledge Extraction System from English Newspaper

Web has tens of millions of files on any subject matter which can be from any field. It may be very tough for anybody to examine loads of documents to apprehend knowledge approximately any occasion. The aim of my researc...

Download PDF file
  • EP ID EP705205
  • DOI 10.31142/etj/v4i7.01
  • Views 7
  • Downloads 0

How To Cite

Carlos Eduardo Belman López,    José Alfredo Jiménez García,    José Antonio Vázquez López  (2019). COMPARISON OF THE PREDICTION ACCURACY THRU ARTIFICIAL NEURAL NETWORKS WITH RESPECT TO MULTIPLE LINEAR REGRESSION USING R. Engineering and Technology Journal, 4(7), -. https://europub.co.uk/articles/-A-705205