2-3 September 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation
Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0100360
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.
Abstract: In this research we analyzed the performance of some well-known classification algorithms in terms of their accuracy and proposed a methodology for model stacking on the basis of their correlation which improves the accuracy of these algorithms. We selected; Support Vector Machines (svm), Naïve Bayes (nb), k-Nearest Neighbors (knn), Generalized Linear Model (glm), Latent Discriminant Analysis (lda), gbm, Recursive Partitioning and Regression Trees (rpart), rda, Neural Networks (nnet) and Conditional Inference Trees (ctree) in our research and preformed analyses on three textual datasets of different sizes; Scopus 50,000 instances, IMDB Movie Reviews having 10,000 instances, Amazon Products Reviews having 1000 instances and Yelp dataset having 1000 instances. We used R-Studio for performing experiments. Results show that the performance of all algorithms increased at Meta level. Neural Networks achieved the best results with more than 25% improvement at Meta-Level and outperformed the other evaluated methods with an accuracy of 95.66%, and altogether our model gives far better results than individual algorithms’ performance.
Keywords: Classification algorithms; model stacking; correlation; k-nearest neighbor; pre-processing; meta classifiers
Muhammad Azam, Dr. Tanvir Ahmed, Dr. M. Usman Hashmi, Rehan Ahmad, Abdul Manan, Muhammad Adrees and Fahad Sabah, “Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100360
@article{Azam2019,
title = {Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100360},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100360},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Muhammad Azam and Dr. Tanvir Ahmed and Dr. M. Usman Hashmi and Rehan Ahmad and Abdul Manan and Muhammad Adrees and Fahad Sabah}
}
