Incremental Naïve Bayesian Learning Algorithm based on Classification Contribution Degree

Ren, Shouxin; Lian, Yangyang; Zou, Xiaojian

doi:10.4304/jcp.9.8.1967-1974

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…The first method utilized for comparison is a incremental naive Bayesian classifier [58]. We use exactly the same parameters as [58] in the experiments.…”

Section: Comparison Methodsmentioning

confidence: 99%

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Argumentation-Based Online Incremental Learning

Ayoobi

Cao

Verbrugge

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot's programmer cannot predict all the possible external failures which the robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle external failures originating from a change in the environment. Existing research typically offers special-purpose solutions. Furthermore, the current incremental online learning algorithms cannot generalize well with just a few observations. In contrast, our method extracts a set of hypotheses, which can then be used for finding the best recovery behavior at each failure state. The proposed argumentation-based online incremental learning approach uses an abstract and bipolar argumentation framework to extract the most relevant hypotheses and model the defeasibility relation between them. This leads to a novel online incremental learning approach that overcomes the addressed problems and can be used in different domains including robotic applications. We have compared our proposed approach with state-of-the-art online incremental learning approaches, an approximation-based reinforcement learning method, and several online contextual bandit algorithms. The experimental results show that our approach learns more quickly with a lower number of observations and also has higher final precision than the other methods.Note to Practitioners-This work proposes an online incremental learning method that learns faster by using a lower number of failure states than other state-of-the-art approaches. The resulting technique also has higher final learning precision than other methods. Argumentation-based online incremental learning generates an explainable set of rules which can be further used for human-robot interaction. Moreover, testing the proposed method using a publicly available dataset suggests wider applicability of the proposed incremental learning method outside the robotics field wherever an online incremental learner is required. The limitation of the proposed method is that it aims for handling discrete feature values.

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“…The first method utilized for comparison is a incremental naive Bayesian classifier [58]. We use exactly the same parameters as [58] in the experiments.…”

Section: Comparison Methodsmentioning

confidence: 99%

“…The first method utilized for comparison is a incremental naive Bayesian classifier [58]. We use exactly the same parameters as [58] in the experiments. The second categories of methods that are used for comparison are rule extraction and decision-tree based methods.…”

Section: Comparison Methodsmentioning

confidence: 99%

Argumentation-Based Online Incremental Learning

Ayoobi

Cao

Verbrugge

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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“…WSN-DS is an imbalanced dataset. Table 1 shows a brief description of the three datasets, and Tables (2)(3)(4) shows Type of attack classes distributions with the results per class. Lastly, we benchmarked our experiments against 57 different datasets obtained from the UCI repository [42].…”

Section: Datasetsmentioning

confidence: 99%

“…These properties make NB suitable for intrusion detection/prevention systems to detect new unknown "zero day" attack signatures. Incremental learning allows the classifier to amend new malicious training instances only to refine and improve the prediction efficiently without training the classifier on the whole dataset again [2,3,4]. Given a query instance of the form < 𝑎𝑎 1 , 𝑎𝑎 2 , ⋯ , 𝑎𝑎 𝑚𝑚 >, where 𝑎𝑎 𝑗𝑗 is the j 𝑡𝑡ℎ attribute value, the algorithm uses Eq.…”

Section: Introductionmentioning

confidence: 99%

Complement Class Fine-Tuning of Naïve Bayes for Severely Imbalanced Datasets

Alenazi¹,

Hindi²,

AsSadhan³

2022

Preprint

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In this work, we adapt the fine-tuning algorithm of the Naïve Bayesian (FTNB) classifier to make it more suitable for imbalanced datasets. In particular, we boost misclassified instance probability terms by an amount that is disproportional to the harmonic mean of actual and predicted classes. The intuition is that discriminative attributes when the instance is misclassified would have small probability term pair values in both the actual class due to data scarcity and the predicted class due to weak correlation. Conversely, if both values are relatively high, then the attribute has good data coverage (support) and it should not be a cause for misclassification. Since the harmonic average is dominated by the smaller value and we have an imbalanced dataset, we should enact a large update if both or either term probabilities of actual and predicted classes are small. We used several benchmark datasets (60 different balanced and imbalanced datasets) to determine if the poor performance of the NB classifier is due to the scarcity of data and compared the performance of the proposed algorithm with NB, original FTNB, and other relatively new SOTA Ensemble Imbalanced Classifiers. Our empirical results reveal that the new proposed algorithm significantly outperforms all other classifiers.

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“…The posterior probability is estimated from the prior probability and existing data. The predicted posterior probability will then become the new prior probability for the next learning batch [52,53]. Subsequently, the incremental learning algorithm saves the updated prior probability as knowledge.…”

Section: Incremental Naïve Bayes Modelmentioning

confidence: 99%

An Online Security Prediction and Control Framework for Modern Power Grids

2021

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

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Incremental Naïve Bayesian Learning Algorithm based on Classification Contribution Degree

Cited by 15 publications

References 14 publications

Argumentation-Based Online Incremental Learning

Argumentation-Based Online Incremental Learning

Complement Class Fine-Tuning of Naïve Bayes for Severely Imbalanced Datasets

An Online Security Prediction and Control Framework for Modern Power Grids

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