A new weighted naive Bayes method based on information diffusion for software defect prediction

The outbreak of Coronavirus has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

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“…Finally, the target class for the input case IC can be calculated by using (12) (Ji et al 2019). ( 9)…”

Section: Feature Correlated Naïve Bayes Phase (Fcnbp)mentioning

confidence: 99%

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour¹,

Saleh

Badawy

et al. 2021

J Ambient Intell Human Comput

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“…The WNB model assigns different weights to nodes to strengthen the connections between nodes. In this way, the assumption of the independence of NB can be weakened (Huang et al, 2015;Lee, 2015;Liu R. et al, 2017;Tang et al, 2018;Ji et al, 2019). The structures of BNW, NB, and WNB models are shown in Figure 3.…”

Section: Methodology Weighted Naïve Bayesmentioning

confidence: 99%

A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

Xiang

Zhou

et al. 2022

Front. Energy Res.

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In order to improve the wildfire prevention capacity of transmission lines, a wildfire risk assessment method for transmission-line corridors based on Weighted Naïve Bayes (WNB) is proposed in this paper. Firstly, the importance of 14 collected types of wildfire-related factors is analyzed based on the information gain ratio. Then, the optimal factors set and the most accurate sampling table are constructed by deleting the factors in the lowest order of importance one by one. Finally, the performance of the WNB model is compared to that of NB and BNW models according to the ROC curve and visualization. A total of 76.36% of fire events in 2020 fell in high-risk and very-high-risk regions, indicating the acceptable accuracy of the proposed assessment method of wildfire risk.

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“…Besides, Asmono et al [25] proposed a feature weighting NBC to predict software defects by employing the absolute value of the correlation coefficient as the weighting technique. Recently, Huang et al [26] utilized information diffusion as weights of feature to learn NBC.…”

Section: Nbc For Software Defect Predictionmentioning

confidence: 99%

“…Some approaches proposed in Ref. [23][24][25][26] belong to the feature weighting NBC model to predict software defects. Intuitively, the highly predictive features should be assigned bigger weights when training a prediction model.…”

Section: Introductionmentioning

confidence: 99%

Correlation feature and instance weights transfer learning for cross project software defect prediction

Zou

Qiu

et al. 2021

IET Software

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Due to the differentiation between training and testing data in the feature space, crossproject defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot-spot for building classifiers in the target domain using the data from the related source domains. To implement better CPDP models, recent studies focus on either feature transferring or instance transferring to weaken the impact of irrelevant cross-project data. Instead, this work proposes a dual weighting mechanism to aid the learning process, considering both feature transferring and instance transferring. In our method, a local data gravitation between source and target domains determines instance weight, while features that are highly correlated with the learning task, uncorrelated with other features and minimizing the difference between the domains are rewarded with a higher feature weight. Experiments on 25 real-world datasets indicate that the proposed approach outperforms the existing CPDP methods in most cases. By assigning weights based on the different contribution of features and instances to the predictor, the proposed approach is able to build a better CPDP model and demonstrates substantial improvements over the stateof-the-art CPDP models.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A new weighted naive Bayes method based on information diffusion for software defect prediction

Cited by 26 publications

References 52 publications

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

Correlation feature and instance weights transfer learning for cross project software defect prediction

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