A New Three-Way Incremental Naive Bayes Classifier

Abstract

Aiming at the problems of the dynamic increase in data in real life and that the naive Bayes (NB) classifier only accepts or rejects the sample processing results, resulting in a high error rate when dealing with uncertain data, this paper combines three-way decision and incremental learning, and a new three-way incremental naive Bayes classifier (3WD-INB) is proposed. First, the NB classifier is established, and the distribution fitting is carried out according to the minimum residual sum of squares (RSS) for continuous data, so that 3WD-INB can process both discrete data and continuous data, then carry out an incremental learning operation, select the samples with higher data quality according to the confidence of the samples in the incremental training set for incremental learning, solve the problem of data dynamics and filter the poor samples. Then we construct the 3WD-INB classifier and determine the classification rules of the positive, negative and boundary domains of the 3WD-INB classifier, so that the three-way classification of samples can be realized and better decisions can be made when dealing with uncertain data. Finally, five discrete data and five continuous data are selected for comparative experimental analysis with traditional classification methods. The results show that 3WD-INB has high accuracy and recall rate on different types of datasets, and the classification performance is also relatively stable.

An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

“…The local model under the framework of the FTwNB algorithm is the 3WD-INB. The 3WD-INB was proposed by Yang et al [29]. The algorithm uses the idea of the three-way decision to realize the three-way classification of samples, making the classification process more in line with people's thought process.…”

“…The process for the three-way classification is completed through the parameters α and β (generally: 0 ≤ β ≤ α ≤ 1)), and the P(C c ), P C c , P v According to the derivation by Yang et al [29], the expressions of the POS domain, NEG domain and BND domain of the 3WD-INB are shown in (8). According to this classification rule, the three-way classification of sample x h can be realized.…”

“…For the evaluation indicators under the three-way decision systems, drawing on the evaluation methods of Yang et al [29] and Jia et al [30], the F1 score (F1) and precision (Precision) are used to evaluate the classification performance. The confusion matrix of the three decisions is shown in Table 2.…”

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“…The "Naïve Bayes Classifier" refers to straightforward probability-based classifier which utilizes Bayes' theorem with dependable assumptions of independence. It anticipates that the proximity or lack of a particular class component will depend on the proximity or lack of any other element [19]. Conditional probabilities are necessary for the Naive Bayes calculation.…”

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“…Zhi et al [14] introduced a three-way concept for conflict analysis [15]. Three-way concept analysis was proposed in [15] by combining three-way decision theory [16][17][18][19][20] and formal concept analysis [21,22], with the ability of describing the properties that objects possess in common and those that they do not possess in common [23,24]. Zhi et al [14] analyzed the conflict relationships between agents and identified the binary relationships between sets of agents and sets of issues by using three-way concepts, and then analyzed the causes of conflict.…”