Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

Kim, Taeheung; Lee, Jong-Seok

doi:10.1109/access.2022.3155231

Cited by 24 publications

(10 citation statements)

References 25 publications

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“…Maximum likelihood estimation is used to compute σ c 2 and μ c . Apart from its naive assumption of conditional independence, Naive Bayes classification has shown good performances in many complex real-world problems [21,22].…”

Section: Each Class's Answer Assignment In Naïve Bayesmentioning

confidence: 99%

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Discriminant Feature Trace Transform for Predictive Object Rotation

2023

IJIES

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This paper presents how to modify the proposed Discriminant Feature Trace Transform (DFTF) algorithm for object rotation. This proposed DFTF algorithm based on the Trace Transform (TF) domain can collect data for the formation of distinctive features and is suitable for predicting the rotational direction of an object with the supervised machine learning. TF domain is used after removing the background image that transformation properties can be generated a feature referred to the rotation features of objects inside. After that, the proposed DFTF algorithm transforms the two-dimensional data of each image from the TF domain into the one-dimensional data that can indicate the direction of object rotation within the image. The results of the DFTF algorithm, which is a 1D data vector from each image, are then generated into the labeled datasets for machine learning algorithms including with the Naïve Bayes (NB) for predicting the rotation direction and Random Forest (RF) to reinforce the predicted values from the NB in the form of quadrant in which the interested object is rotated. The simulation experiments are conducted with two types of databases. The first experiment combines with the three different datasets which the results are very effective and can provide the average accuracy rate for all databases up to 99.8%. The second experiment is derived from the visualization of the water bottle production line. It measures the accuracy of the bottled water transfer to the second station. The proposed approach is an accuracy rate up to 92.2%.

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Section: Each Class's Answer Assignment In Naïve Bayesmentioning

confidence: 99%

“…To illustrate the efficiency of proposed DFTF algorithm, we also adopt the similar measurement to other frameworks using the following criteria: Accuracy= TP+TN TP+FN+TN+FP (21) Recall= TP TP+FN (22) Precision= TP TP+FP (23)…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

Discriminant Feature Trace Transform for Predictive Object Rotation

2023

IJIES

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“…4) Naive Bayes (NB) algorithm [13]. It is one of the most widely used classification algorithms, with simple logic, easy implementation, and low time and space costs during the classification process.…”

Section: Comparison Algorithmmentioning

confidence: 99%

Research on concept drift data stream classification based on adaptive online broad learning system

Sun,

Guo

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Traditional online broad learning system are unable to adapt to dynamic changes in data streams for concept drift data stream classification. To address this issue, this paper proposes an Adaptive Online Broad Learning System (AOBLS) classification algorithm that introduces an adaptive forgetting factor and concept drift detection mechanism. Additionally, this paper proposes a concept drift index to measure the degree of concept drift. By combining the concept drift index and forgetting factor, the model can adaptively adjust the size of the forgetting factor to better handle concept drift. Experimental results demonstrate that the proposed algorithm performs better than similar algorithms in terms of classification accuracy, stability, and concept drift adaptability.

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“…But determining the parameters of NBWELM is a challenging task. Attribute weighting methods are proposed to minimize different loss functions [21]. The loss function belongs to an exponential function family, which makes the optimization problem easier to solve, thereby enhancing the robustness of the naive Bayes classifier.…”

Section: Introductionmentioning

confidence: 99%

A two-stage optimized robust kernel density estimation for Bayesian classification with outliers

Wei,

Peng,

et al. 2024

Preprint

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A Bayesian classifier based on kernel density estimation (KDE) is an effective routine for continuous data classification. However, the presence of outliers and inappropriate selection of bandwidth may cause inaccurate and distorted estimations of the density function. This paper presents an improved Bayesian classification approach by employing an optimized robust kernel density estimation (ORKDE). The impact of outliers can be down-weighted by a two-stage optimization in the conditional probability density calculation. In the first stage, an improved hybrid rice optimization (HRO) algorithm is proposed for selecting the bandwidth of a Gaussian KDE using a mixed cross-validation function. It incorporates Levy flight and elite reverse learning strategy to enhance the global optimization searching capability. In the second stage, a soft-redescending Welsch function is applied as the loss function to reduce the weights of outliers using the M-estimation in the kernel space. The relative efficiency index is used to derive the tuning parameter of the influence function. A kernelized iteratively re-weighted least-squares procedure employs both the tuning parameter and the quantile rule for effectively calculating the optimal weights of samples. With the proposed ORKDE method, the Naive Bayesian classifier enhances the generalization in the classification with outliers. The effectiveness of our method is demonstrated by the experiments using synthetic and real-world datasets.

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Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

Cited by 24 publications

References 25 publications

Discriminant Feature Trace Transform for Predictive Object Rotation

Discriminant Feature Trace Transform for Predictive Object Rotation

Research on concept drift data stream classification based on adaptive online broad learning system

A two-stage optimized robust kernel density estimation for Bayesian classification with outliers

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