Not so greedy: Randomly Selected Naive Bayes

Authors:
Liangxiao Jiang;Zhihua Cai;Harry Zhang;Dianhong Wang
Affiliations:
Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China;Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China;Department of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada E3B 5A3;Department of Electronic Engineering, China University of Geosciences, Wuhan, Hubei 430074, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 20
Cited 0

Bagging predictors

Machine Learning
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Machine Learning
Random Forests

Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking

Machine Learning
Inference for the Generalization Error

Machine Learning
Active Sampling for Class Probability Estimation and Ranking

Machine Learning
Learning Bayesian network classifiers by maximizing conditional likelihood

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A decision tree-based attribute weighting filter for naive Bayes

Knowledge-Based Systems
Learning decision tree for ranking

Knowledge and Information Systems
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Improving algorithms for structure learning in Bayesian Networks using a new implicit score

Expert Systems with Applications: An International Journal
Random one-dependence estimators

Pattern Recognition Letters
Constructing the Bayesian network structure from dependencies implied in multiple relational schemas

Expert Systems with Applications: An International Journal
Induction of selective Bayesian classifiers

UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

Expert Systems with Applications: An International Journal
Learning naive bayes for probability estimation by feature selection

AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence

Quantified Score

Hi-index	12.05

Visualization

Abstract

Many approaches are proposed to improve Naive Bayes, among which the attribute selection approach has demonstrated remarkable performance. Algorithms for attribute selection fall into two broad categories: filters and wrappers. Filters use the general data characteristics to evaluate the selected attribute subset before the learning algorithm is run, while wrappers use the learning algorithm itself as a black box to evaluate the selected attribute subset. In this paper, we work on the attribute selection approach of wrapper and propose an improved Naive Bayes algorithm by carrying a random search through the whole space of attributes. We simply called it Randomly Selected Naive Bayes (RSNB). In order to meet the need of classification, ranking, and class probability estimation, we discriminatively design three different versions: RSNB-ACC, RSNB-AUC, and RSNB-CLL. The experimental results based on a large number of UCI datasets validate their effectiveness in terms of classification accuracy (ACC), area under the ROC curve (AUC), and conditional log likelihood (CLL), respectively.