Classifiers Evaluation: Comparison of Performance Classifiers Based on Tuples Amount

Mochammad Yusa, Ema Utami

Abstract


The  aim  of  this  study  is  to  compare  some classifiers’ performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features and 49.736 tuples. The  methodology  of  this  research  starts  from  preprocessing phase. After that, the clean dataset is divided into 5 subsets which represent every multiple of 10.000 tuples randomly. Each particular subset will be validated by three traditional classifiers i.e. Naive Bayes, K-Nearest Neighbor (k-NN), and Decision Tree. We also implement some setting parameters of each classifier except Naïve Bayes. Validation method used in this research is 10-Fold Cross-Validation. As the final conclusion, we compare the performance of classifiers based on the number of tuples. Our study indicates that the more the number of tuples, the lower and weaker the MAE and Accuracy performances whereas the kappa statistic performance tend to be fluctuated. Our study also found that Naïve Bayes outperforms k-NN and Decision Tree in overall. The top classifiers performances were reached in a 20.000-tuple evaluation.

The aim of this study  is to compare some classifiers performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The r


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