Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

Huang, Jin; Lu, Jun; Ling, Charles X.

doi:10.1109/icdm.2003.1250975

Cited by 171 publications

(113 citation statements)

References 8 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…These categories are statistical-based, distancebased, decision tree-based, neural network-based, and rule-based. Each category consists of several algorithms, but the most popular from each category that are used extensively are C4.5, Naïve Bayes, K-Nearest Neighbors, and Backpropagation Neural Network [2,9,10].…”

Section: Methodsmentioning

confidence: 99%

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Karim¹,

Rahman²

2013

JSEA

102

View full text Add to dashboard Cite

Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naïve Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.

show abstract

Section: Methodsmentioning

confidence: 99%

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Karim¹,

Rahman²

2013

JSEA

102

View full text Add to dashboard Cite

show abstract

“…In comparison with the SVM both kNN and Naive Bayes are very simple and easily assimilated. The Naive Bayes classifier is superior in terms of CPU and memory consumption, as shown by [13]. In many cases their performance is very close to the most complicated techniques.…”

Section: E Classificationmentioning

confidence: 87%

Social Data Analysis of Brazilian’s Mood from Twitter

Prata¹,

Soares²,

Silva³

et al. 2016

IJSSH

View full text Add to dashboard Cite

Abstract-In this work, a software application was developed to analyze and visualize messages over Twitter social network, ranking the posts relatively to variations in moods within the Brazilian territory. Artificial intelligence techniques such as text mining and sentiment analysis were used for this purpose. The use of methods of machine learning allows determining the polarity (positive or negative) of tweets collected. Results were displayed in cartograms, through representations of tweet's geographic locations. Surprisingly, another study of twitter's mood from United States Nation showed similar results for the variation of moods throughout the day, hypothesizing a humor pattern for human beings during the period of 24 hours.

show abstract

“…Several research papers indicate the fact that in some cases for a given dataset, the learning method that obtains the best model according to a given measure, is not the best method if a different measure is used. In [25] is shown that Naive Bayes and pruned decision trees are very similar in predictive accuracy. Later on, applying the same The predictive accuracy is perhaps the most popular metric for classification problems and many researchers have been publishing papers that show the performance of various algorithms, techniques and transformations in terms of predictive accuracy on the same datasets that we also use in this paper.…”

Section: A Performance Metricmentioning

confidence: 99%

Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Zdravevski¹,

Lameski²,

Kulakov³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus the need of transformation from nominal to numeric data. Weight of evidence (WoE) is one of the parameters that can be used for transformation of the nominal features to numeric. In this paper we describe a method that uses WoE to transform the features. Although the applicability of this method is researched to some extent, in this paper we extend its applicability for multi-class problems, which is a novelty. We compared it with the method that generates dummy features. We test both methods on binary and multi-class classification problems with different machine learning algorithms. Our experiments show that the WoE based transformation generates smaller number of features compared to the technique based on generation of dummy features while also improving the classification accuracy, reducing memory complexity and shortening the execution time. Be that as it may, we also point out some of its weaknesses and make some recommendations when to use the method based on dummy features generation instead.

show abstract

Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

Cited by 171 publications

References 8 publications

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Social Data Analysis of Brazilian’s Mood from Twitter

Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Contact Info

Product

Resources

About

Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

Cited by 171 publications

References 8 publications

Decision Tree and Na&#239;ve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Na&#239;ve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Social Data Analysis of Brazilian’s Mood from Twitter

Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Contact Info

Product

Resources

About

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing