SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning

Xie, Zhipeng; Hsu, Wynne; Liu, Zongtian; Lee, Mong Li

doi:10.1007/3-540-47887-6_10

Cited by 64 publications

(37 citation statements)

References 9 publications

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“…Previously, Xie et al (2002) proposed an improved algorithm called Selective Neighborhood Naïve Bayes (SNNB). Lazkano and Sierra (2003) proposed the combination in a different way that combines the nearest neighbor with Bayesian network.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

Ferdousy¹,

Islam²,

Matin³

2013

CIS

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In this study, we present a new classifier that combines the distance-based algorithm K-Nearest Neighbor and statistical based Naïve Bayes Classifier. That is equipped with the power of both but avoid their weakness. The performance of the proposed algorithm in terms of accuracy is experimented on some standard datasets from the machine-learning repository of University of California and compared with some of the art algorithms. The experiments show that in most of the cases the proposed algorithm outperforms the other to some extent. Finally we apply the algorithm for predicting profitability positions of some financial institutions of Bangladesh using data provided by the central bank.

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Section: The Proposed Algorithmmentioning

confidence: 99%

Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

Ferdousy¹,

Islam²,

Matin³

2013

CIS

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“…It is very efficient with reasonable prediction accuracy [8], [9], [10], [11], [12], [13], [14], [15]. In recent years, there has also been considerable interest in developing variants of NB that weaken the attribute independence assumption in order to further improve the prediction accuracy [6], [7], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30]. For instance, one-dependence estimators (ODEs) [23] such as the tree-augmented naive Bayes (TAN) [16] provide a powerful alternative to NB.…”

Section: Spodementioning

confidence: 99%

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

Yang

Webb

Cerquides

et al. 2007

IEEE Trans. Knowl. Data Eng.

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Abstract-We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

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“…SNNB [18] and LWNB [10] select k-nearest neighbor examples from original training data set to construct sub data set according the attribute value vector of the tested instance, and with which a local naive Bayesian classifier is trained. LBR [20] picks out the attributes which remarkably improve the classifier accuracy, and constructs local naive Bayesian classifier using the remainder attributes.…”

Section: Introductionmentioning

confidence: 99%

A Double Layer Bayesian Classifier

Sun

Wang

Chen

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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Numerous approaches have been proposed to relax the conditional independence assumption of naive bayes, the accuracy performance was indeed improved relative to naive bayes when the assumption is violated. But most of the previous approaches treated the attribute relation in the same way for all class labels. In practice, this relation may be different for different class labels. This paper proposes a novel approach, by which the posterior probability of different class label is evaluated using different attribute relation. Experiment results indicate that the new approach obtains comparative performance relative to other modern Bayesian classifiers on some datasets, and on some other datasets it outperforms the others.

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SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning

Cited by 64 publications

References 9 publications

Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

A Double Layer Bayesian Classifier

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