Then, a hybrid rule-based classification model is developed by integrating the knowledge-driven rule base and the rule base learned from the training data using genetic algorithm. Experiments based on real datasets demonstrate the superiority of the proposed classification model.
Mar 18, 2022
The hybrid decision tree is able to remove noisy data to avoid overfitting. · The hybrid Bayes classifier identifies a subset of attributes for classification.
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Experimental results show that the hybrid classifier performs better than a purely gener- ative classifier (naive Bayes) or a purely discriminative clas- sifier ...
PDF | In this paper, we introduce a new restricted Bayesian network classifier that extends naive Bayes by relaxing the conditional independence.
Based on neural networks and fuzzy set theory, a hybrid Bayesian optimal classifier is proposed in the paper. It can implement fuzzy operation, and generate ...
This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy that learns from quantitative data and induces a fuzzy rule based model ...
It is essential to propose a rule based classification technique with machine learning to predict the heart disease. The proposed work is implemented in python ...
This paper discusses a novel hybrid approach for text cate- gorization that combines a machine learning algorithm, which provides a base model trained with a ...
Bayesian Classification - an overview | ScienceDirect Topics
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Bayesian classification is a probabilistic approach to learning and inference based on a different view of what it means to learn from data.
Oct 22, 2024 · In this work a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by ...