Performance Test on Classification Algorithms
Jeevitha Sampath1, Sunitha N V2, Arpana Shetty3
1Jeevitha Sampath, Department of CSE BIT, Mangalore (Karnataka), India.
2Sunitha N V, Department of CSE BIT, Mangalore (Karnataka), India.
3Arpana Shetty, Department of CSE BIT, Mangalore (Karnataka), India.
Manuscript received on 21 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 914-917 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11720782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1172.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, a huge amount of data is generated due to the growth in the technologies. There are different tools used to view this massive amount of data, and these tools contain different data mining techniques which can be applied for the obtained data sets. Classification is required to extract useful information or to predict the result from these enormous amounts of data. For this purpose, there are different classification algorithms. In this paper, we have compared Naive Bayes, K*, and random forest classification algorithm using Weka tool. To analyze the performance of these three algorithms we have considered three data sets. They are diabetes, supermarket and weather data set. In this work, an analysis is made based on the confusion matrix and different performance measures like RMSE, MAE, ROC, etc.
Keywords: Naive Bayes, K*, Random Forest, Root Mean Squared Error (RMSE), Mean Absolute Error(MAE), Receiver Operating Characteristic(ROC), Weka.
Scope of the Article: Classification