Comparing Fuzzy Naive Bayes and Gaussian Naive Bayes for Decision Making in RoboCup 3D

Bustamante, Carlos D.; Garrido, Leonardo; Soto, Rogelio

doi:10.1007/11925231_23

Cited by 32 publications

(15 citation statements)

References 5 publications

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“…In this section, we will talk about the classification result based on classification algorithm, i.e., Ada Boost Algorithm (ADB) [65][66][67], Multilayer Perceptron Algorithm (MLP) [68][69][70], Gaussian NB Algorithm (GNB) [71][72][73], standard Support Vector Machine Algorithm (SVM) [58][59][60], existing literature methods and proposed scalable-kernel based SVM algorithm.…”

Section: Resultsmentioning

confidence: 99%

Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Ketu

Mishra

2021

Complex Intell. Syst.

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In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

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Section: Resultsmentioning

confidence: 99%

Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Ketu

Mishra

2021

Complex Intell. Syst.

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“…We make comparison of classification results of the proposed approach and other approaches with fuzzy classifiers for the same initial data. Four classifiers are involved in this comparison: Fuzzy Naïve Bayes classifier [80], fuzzy classification rules according to the algorithm in [81] and [82], and neural network (more specifically, Fuzzy Multi-Layer Perceptron) [83]. We implement all these classifiers in MATLAB.…”

Section: Youden's Indexmentioning

confidence: 99%

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

2020

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The analysis of EEG signal is a relevant problem in health informatics, and its development can help in detection of epileptic's seizures. The diagnosis is based on classification of EEG signal. Different methods and algorithms for classification of EEG signals with an accepted level of reliability and accuracy have been developed over years. All these methods have two steps that are signal preprocessing and classification. The goal of the preprocessing step is removing noise and reduction of the initial signal dimensionality. The signal dimensionality reduction is required by classification methods, but its result is a loss of small information before the classification. In this paper, an approach for EEG signal classification that takes this loss of information into account is considered. The novelty of the considered approach is usage of fuzzy classifier in the classification step. This classifier allows taking uncertainty of initial data into account, which is caused by loss of some information during dimensionality reduction of initial signal. However, application of fuzzy classifier needs modification of the preprocessing step because it requires data in fuzzy form. Therefore, fuzzification procedure is added to the preprocessing step. In this paper, Fuzzy Decision Tree (FDT) is used as the fuzzy classifier for the epileptic's seizure detection. Its application allows achieving 99.5% accuracy of the classification of epileptic's seizure. The comparison with other studies shows that FDT is very effective for task of epileptic's seizure detection.

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“…Bustamante et al [3] compared Gaussian distribution and Fuzzy Naive Bayes classifier methods for "RoboCup Soccer Simulation 3D" domain. Qin et al [4] proposed a new method which uses Gaussian distribution as conditional probability to handle uncertain numerical data using Bayes theorem.…”

Section: Related Workmentioning

confidence: 99%

Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions

Yıldırım

Birant

2014

2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings

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In data mining, when using Naive Bayes classification technique, it is necessary to overcome the problem of how to deal with continuous attributes. Most previous work has solved the problem either by using discretization, normal method or kernel method. This study proposes the usage of different continuous probability distribution techniques for Naive Bayes classification. It explores various probability density functions of distributions. The experimental results show that the proposed probability distributions also classify continuous data with potentially high accuracy. In addition, this paper introduces a novel method, named NBC4D, which offers a new approach for classification by applying different distribution types on different attributes. The results (obtained classification accuracy rates) show that our proposed method (the usage of more than one distribution types) has success on real-world datasets when compared with the usage of only one well known distribution type.

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Comparing Fuzzy Naive Bayes and Gaussian Naive Bayes for Decision Making in RoboCup 3D

Cited by 32 publications

References 5 publications

Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions

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