Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 3
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.
Authors and Affiliations
Muhammad Azam, Dr. Tanvir Ahmed, Dr. M. Usman Hashmi, Rehan Ahmad, Abdul Manan, Fahad Sabah
Keywords
Related Articles
- EP ID EP499588
- DOI 10.14569/IJACSA.2019.0100360
- Views 93
- Downloads 0
Impact of Distributed Generation on the Reliability of Local Distribution System
With the growth of distributed generation (DG) and renewable energy resources the power sector is becoming more sophisticated, distributed generation technologies with its diverse impacts on power system is becoming attr...
Multi- Spectrum Bands Allocation for Time-Varying Traffic in the Flexible Optical Network
The flexible optical networks are the promising solution to the exponential increase of traffic generated by telecommunications networks. They combine flexibility with the finest granularity of optical resources. Therefo...
Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods
Comparative study on discrimination methods for identifying dangerous red tide species based on wavelet utilized classification methods is conducted. Through experiments, it is found that classification performance with...
The Impact of Flyweight and Proxy Design Patterns on Software Efficiency: An Empirical Evaluation
In this era of technology, delivering quality software has become a crucial requirement for the developers. Quality software is able to help an organization to success and gain a competitive edge in the market. There are...
Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study
Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threat...