A Decision tree-based attribute weighting filter for naive Bayes

The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness--the assumption that attributes are independent given the class. All of them improve the performance of naïve Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its run-time complexity and the fact that it maintains the simplicity of the final model

Absolute Correlation Weighted Naïve Bayes for Software Defect Prediction

The maintenance phase of the software project can be very expensive for the developer team and harmful to the users because some flawed software modules. It can be avoided by detecting defects as early as possible. Software defect prediction will provide an opportunity for the developer team to test modules or files that have a high probability defect. Naïve Bayes has been used to predict software defects. However, Naive Bayes assumes all attributes are equally important and are not related each other while, in fact, this assumption is not true in many cases. Absolute value of correlation coefficient has been proposed as weighting method to overcome Naïve Bayes assumptions. In this study, Absolute Correlation Weighted Naïve Bayes have been proposed. The results of parametric test on experiment results show that the proposed method improve the performance of Naïve Bayes for classifying defect-prone on software defect prediction

Locally weighted learning: How and when does it work in Bayesian networks?

© 2016, Taylor and Francis Ltd. All rights reserved. Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC’s primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting

Seir immune strategy for instance weighted naive bayes classification

© Springer International Publishing Switzerland 2015. Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods

A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes

In many classification models, data is discretized to better estimate its distribution. Existing discretization methods often target at maximizing the discriminant power of discretized data, while overlooking the fact that the primary target of data discretization in classification is to improve the generalization performance. As a result, the data tend to be over-split into many small bins since the data without discretization retain the maximal discriminant information. Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data. More specifically, the Max-Dependency criterion maximizes the statistical dependency between the discretized data and the classification variable while the Min-Divergence criterion explicitly minimizes the JS-divergence between the training data and the validation data for a given discretization scheme. The proposed MDmD criterion is technically appealing, but it is difficult to reliably estimate the high-order joint distributions of attributes and the classification variable. We hence further propose a more practical solution, Max-Relevance-Min-Divergence (MRmD) discretization scheme, where each attribute is discretized separately, by simultaneously maximizing the discriminant information and the generalization ability of the discretized data. The proposed MRmD is compared with the state-of-the-art discretization algorithms under the naive Bayes classification framework on 45 machine-learning benchmark datasets. It significantly outperforms all the compared methods on most of the datasets.Comment: Under major revision of Pattern Recognitio

Optimized Naïve Bayesian Algorithm for Efficient Performance

Naïve Bayesian algorithm is a data mining algorithm that depicts relationship between data objects using probabilistic method. Classification using Bayesian algorithm is usually done by finding the class that has the highest probability value. Data mining is a popular research area that consists of algorithm development and pattern extraction from database using different algorithms. Classification is one of the major tasks of data mining which aimed at building a model (classifier) that can be used to predict unknown class labels. There are so many algorithms for classification such as decision tree classifier, neural network, rule induction and naïve Bayesian. This paper is focused on naïve Bayesian algorithm which is a classical algorithm for classifying categorical data. It easily converged at local optima. Particle Swarm Optimization (PSO) algorithm has gained recognition in many fields of human endeavours and has been applied to enhance efficiency and accuracy in different problem domain. This paper proposed an optimized naïve Bayesian classifier using particle swarm optimization to overcome the problem of premature convergence and to improve the efficiency of the naïve Bayesian algorithm. The classification result from the optimized naïve Bayesian when compared with the traditional algorithm showed a better performance Keywords: Data Mining, Classification, Particle Swarm Optimization, Naïve Bayesian