Using weighted majority voting classifier combination for relation classification in biomedical texts

Remya, K R; Ramya, J.

doi:10.1109/iccicct.2014.6993144

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…Text processing using deep learning model, feature selection using support vector machine, data pre-processing etc. were mentioned approach to apply [8], [9], [10], [11], [12]. We have proposed to use deep neural network to predict job scams.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study of Fake Job Posts Using Different Data Mining Techniques

2023

IJPREMS

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In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like Logistic Regression, support vector machine, naive bayes classifier, random forest classifier, to predict ajob post if it is real or fraudulent. Our application was built which takes Job Id, Description and job Requirements to predict whether the given job post is real or fake. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. The trained classifier shows approximately 98% classification accuracy to predicts fraudulent job post

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Section: Literature Surveymentioning

confidence: 99%

A Comparative Study of Fake Job Posts Using Different Data Mining Techniques

2023

IJPREMS

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“…There are the classifier ensemble methods combining multiple outputs of multiple models to increase the performance of the prediction. There are many basic [22], [29] to advanced ensemble methods [30], [31]. However, in this paper, we use the majority voting method.…”

Section: Related Workmentioning

confidence: 99%

Job Prediction: From Deep Neural Network Models to Applications

Huynh¹,

Nguyen²,

Nguyen³

et al. 2020

2020 RIVF International Conference on Computing and Communication Technologies (RIVF)

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Determining the job is suitable for a student or a person looking for work based on their job descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT job dataset. In addition, we proposed a simple and effective ensemble model combining different deep neural network models. Our experimental results illustrated that our proposed ensemble model achieved the highest result with an F1-score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.

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“…where the function argmax returns an integer J, which stands for the index of the maximum voting scores. Equations (3) and (4) is the normal weighted majority voting being wildly used [31]. Every classifier has a different voting weight according to its identification accuracy.…”

Section: Defining a Subject Space {Smentioning

confidence: 99%

Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting

Zeng

Liu

et al. 2019

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This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers.

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Using weighted majority voting classifier combination for relation classification in biomedical texts

Cited by 14 publications

References 5 publications

A Comparative Study of Fake Job Posts Using Different Data Mining Techniques

A Comparative Study of Fake Job Posts Using Different Data Mining Techniques

Job Prediction: From Deep Neural Network Models to Applications

Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting

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