Performance Of Naive Bayes Classifier Research Articles

Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Liver diseases pose a significant health challenge, necessitating robust predictive tools for early diagnosis. This study aims to determine the predictive performance of Naive Bayes classifier, one of the data mining algorithms, in the classification of liver diseases. The study applied 5, 10 and 20-fold cross-validation method. Trying to determine the effect of the cross-validation (CV) method used on the classification performance, this study used the "BUPA" dataset in the UCI Machine Learning Repository database for this purpose. The dataset consists of 6 variables and 345 examples. Orange program was used for data analysis. The study showed that the accuracy of the Naive bayes method were 64.6%, 66.7% and 64.3%, respectively. Accordingly, it can be said that the 10-fold CV method performs better. Compared to similar studies, it can be claimed that the analysis results obtained with the Orange program are better.

Observing Performance of Naive Bayes Classifier on Nursery Dataset

In machine learning, Naive Bayes is a popular technique that is used for classification that is based on the conditional probability of attributes belonging to a label, in which the attribute is selected by select attribute operator in rapid miner. In this paper, the split operator has used that divides the dataset into training and testing. Training is used to train the naïve Bayes and testing is used to evaluate the model. The result shows that this simple model generates a good fit for the nursing dataset. Total accuracy achieved using this method is 87.86% which is not bad.

Comparison Performance of Naive Bayes Classifier and Support Vector Machine Algorithm for Twitter’s Classification of Tokopedia Services

Tokopedia is one of the online shopping centers in Indonesian that carries the business model marketplace. Positive and negative opinions in Twitter from Tokopedia users about company services are source of information for the management. Naive Bayes Classification (NBC) and Support Vectore Machine (SVM) are techniques in data mining used to classify data or users opinion. The algorithm of NBC is very simple since it only use text frequency to compute the posterior probability for each classes. While SVM algorithm is more complex than NBC. SVM develop hyperplane equation which separate data into classes perfectly. The researcher wants to compare the performance of the NBC and SVM algorithms and use them to classify user opinions on Tokopedia’s services, because these two algorithms have different approaches and difficulty levels. Classification included positive and negative class only. Accuracy, precision and recall value are used to compare the performance of both algorithms. Research evaluation shows that SVM linear kernel technique outperform NBC technique with the accuracy 83.34%.

A Study on Impact of Dimensionality Reduction on Naïve Bayes Classifier

Objectives: The time complexity of the machine learning algorithm is directly proportionate to the dimension of the dataset. In this paper, he impacts of dimensionality of the dataset on the machine learning algorithm, Naive-Bayes Classifier is evaluated with all feature subsets to analyze whether there is any variations in the performance. Methods/Statistical Analysis: Naive Bayes Classifier is taken for the study to evaluate its variations in terms of its performance in correctly classified instances and incorrectly classified instances. Pima Indian Type II diabetes dataset is taken for the experimental study. Confusion matrix will be formulated for the performance of Naive-Bayes Classifier using 10-fold cross validation for each run. The study exhibits the impact of the dimensionality on the performance of Naive-Bayes Classifier. Findings: The Naive Bayes classifier classifies the patient records either as diabetes or as non-diabetes using the values of the feature set. It is a probabilistic approach of classifying the patient records into the binary class. It is found that there is an impact on the performance of Naive Bayes Classifier due to the dimensionality of the feature set it terms of Classification accuracy, number of true positives, true negatives, false positives and false negatives. The incorrect classification is certainly dangerous. Whereas the valid classification facilitates the healthcare systems in terms of planning effective course of treatment which will save the life of the patient. The invalid classification will lead to a wrong diagnosis while formulating the treatment plan and it will lead to loss of life. Hence, the invalid classification in terms of false negative rate is to be viewed very seriously. In this paper, the study shows that there is an impact on the performance of Naive Bayes Classifier due to the higher dimensionality of the dataset. Application/Improvements: They will be used in medical Informatics for the quality diagnosis and effective treatment planning. The focus on the false positive rate in the classification accuracy of Naive Bayes Classifier will notably help the healthcare systems to diagnose the patients accurately to save life.