A novel selective naïve Bayes algorithm

Chen, Shenglei; Webb, Geoffrey I.; Liu, Linyuan; Ma, Xin

doi:10.1016/j.knosys.2019.105361

Cited by 304 publications

(132 citation statements)

References 23 publications

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“…The performance of the proposed FF-MVO-RNN was compared with polarity feature over-weighted feature and several existing optimization algorithms like PSO-RNN [44], GWO-RNN [45], MVO-RNN [37] and FF-RNN [38] and the results were computed. Additionally, the performance of the proposed FF-MVO-RNN was compared with various traditional machine learning algorithms like DT [46], NB [47], KNN [48], SVM [49], NN [50], and RNN [51] and the results were computed. Moreover, the performance of the proposed FF-MVO-RNN was analyzed in terms of Accuracy, Sensitivity, Specificity, Precision, FPR, FNR, FDR, NPV, F1 Score and MCCto confirm the performance of the proposed FF-MVO.…”

Section: Methodsmentioning

confidence: 99%

Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

Datta

Chakrabarti

2021

Sādhanā

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In this paper, it is proposed to understand the opinion of the public regarding the policy of demonetization that is implemented recently in India through Aspect-based Sentiment Analysis (ABSA) that predicts the sentiment of specific aspects present in the text. The major aim is to identify the relevant contexts for various aspects. Most of the conventional techniques have adopted attention mechanisms and deep learning concepts that decrease the prediction accuracy and generate huge noise. Another major disadvantage with the attention mechanisms is that the sentiment related to few context words alters with various aspects, and hence it cannot be concluded from itself alone. This paper adopts the optimized deep learning concept for performing the ABSA for demonetization tweets. The proposed model involves various phases such as pre-processing, aspect extraction, polarity feature extraction, and sentiment classification. Initially, the different demonetization tweets collected from the Kaggle dataset are taken. Pre-processing is done with the help of four phases like stop words removal, punctuation removal, lower case conversion, and stemming from minimizing the data to its reduced format. This pre-processed data is further performed with aspect extraction to extract the opinion words. These extracted aspect words are converted to the features with the help of polarity score computation and Word2vec. The weight of the polarity scores is optimized using hybridization of two meta-heuristic algorithms like FireFly Algorithm (FF), and Multi-Verse Optimization (MVO), and the new algorithm is termed as Fire Fly-oriented Multi-Verse Optimizer (FF-MVO). Further, combined features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). As a modification to the existing RNN, the hidden neurons are optimized by the hybrid FF-MVO, FF-MVO-RNN classifies the positive and negative sentiments. Finally, the comparative analysis of different machine learning algorithms proves the competent performance of the proposed model.

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Section: Methodsmentioning

confidence: 99%

Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

Datta

Chakrabarti

2021

Sādhanā

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“…Since Naive Bayes is fast and based on Bayesian statistics, it is efficient at real-time forecasting. Most popular real-time models are based on Bayes statistics [ 48 ]. Naive Bayes works well when the resulting variable extends to more than one class.…”

Section: Proposed Methodsmentioning

confidence: 99%

An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

Müezzinoğlu

Karaköse

2021

Sensors

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The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

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“…In [17], Cubic SVM, Quadratic SVM, and Linear SVM have better performances in predicting the outcome of traumatic brain injury as compared to LR. In [18], NB is the most popular data mining algorithms. Empirical results indicate that the selective NB demonstrates superior classification performance while retaining the simplicity and flexibility at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…NB is a probabilistic ML model. It requires linear parameters in the number of functions of the variables and highly scalable [18]. SVM is an ML algorithm that can be used for classification problems as well as for regression.…”

Section: Classification Of Algorithmsmentioning

confidence: 99%

Identifying the Machine Learning Techniques for Classification of Target Datasets

2020

SJCMS

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Given the dynamic and convoluted nature of numerous datasets, the necessity of enhancing performance outcomes and handling multiple datasets has become more challenging. To handle these issues effectively and improve the quality of multiple approaches, the capabilities of various Machine Learning techniques such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes(NB) and Support Vector Machine (SVM) have been utilized in this study. In this paper, the binary classification method using five different datasets, and many predictor variables have been utilized. Moreover, this research has mainly focused on determining the classification of data into the subsets that share the standard designs. In this regard, many approaches had been studied extensively and used to achieve better yields from the existing literature; however, they were inadequate to provide efficient outcomes. By applying four Supervised ML classification algorithms along with the UCI Datasets of ML Repository, the robustness of the method is progressed. The proposed mechanism is assessed by adopting five performance criteria concerning the accuracy, AUC (Area Under Curve), precision, recall, and F-measure values. The current study experimental results revealed that there is a significant improvement in the confusion matrix rate compared with a similar study and this method can also be used for machine learning problems such as binary classification.

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A novel selective naïve Bayes algorithm

Cited by 304 publications

References 23 publications

Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

Identifying the Machine Learning Techniques for Classification of Target Datasets

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