Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Rupapara, Vaibhav; Rustam, Furqan; Shahzad, Hina Fatima; Mehmood, Arif; Ashraf, Imran; Choi, Gyu Sang

doi:10.1109/access.2021.3083638

Cited by 131 publications

(69 citation statements)

References 36 publications

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“…SV works based on probability of each class as predicted by each model and these probabilities pass through average criteria. In the end, the argmax function is used to average the probabilities to compute the final prediction [58]. In SV model, X 1 , X 2 , and X − 3 are the probabilities by XGB for class 1 (C 1 ), class 2 (C 2 ), and class 3 (C 3 ).…”

Section: Proposed Hard Voting and Soft Voting Modelsmentioning

confidence: 99%

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Siddiqui

Shahzad

Saleem

et al. 2021

Sensors

Self Cite

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Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

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Section: Proposed Hard Voting and Soft Voting Modelsmentioning

confidence: 99%

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Siddiqui

Shahzad

Saleem

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…With the help of confusion matrix performance of proposed model is analysed. The performance of proposed model is analysed using accuracy, precision, recall and f-measure ( Rupapara et al, 2021 ; Jamil et al, 2021 ; Rustam et al, 2021 ). These parameters can be measured with the help of following formulae where TP represents true positives, TN is true negatives, FP shows false positives and FN shows false negatives.…”

Section: Experimental Results Evaluation and Discussionmentioning

confidence: 99%

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

Bilal

Almazroi

Bashir

et al. 2022

PeerJ Computer Science

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Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individually and then evaluations were performed using ensembles. The proposed research was evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms. The analysis of results indicated that the proposed model attained the highest prediction accuracy of 94.7% on the GitHub dataset and 92.43% on the Bigml dataset. State of the art comparison was also performed using the proposed model. The proposed model performed significantly better than state of the art churn prediction models.

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“…Evaluation parameters are used to evaluate the performance of models including precision, F1 Score, recall, and accuracy [51]. These are the commonly used evaluation metrics.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

2021

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With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

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Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Cited by 131 publications

References 36 publications

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

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