Ensemble Learning

List of Articles Ensemble Learning

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1 - A Hybrid Neural Network Ensemble Model for Credit Risk Assessment
shaban elahi Ahmad ghodselahi hamidreza naji
20.1001.1.27170411.1393.6.20.2.0

Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble l More

Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble learning for credit granting decisions is designed. Combining clustering and classification techniques resulted in system improvement. The German bank real dataset was used for neural network training. The proposed model implemented as credit risk evaluation multi agent system and the results showed the proposed model has higher accuracy, better performance and lesser cost in applicant classification when compared with other credit risk evaluation methods Manuscript profile
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2 - Using Sentiment Analysis and Combining Classifiers for Spam Detection in Twitter
mehdi salkhordeh haghighi Aminolah Kermani
20.1001.1.27170411.1401.14.51.10.6

The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one More

The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output will detect spam text or normal text. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods to them, we separately calculate the Tweeter spam detection. Eventually, we enter the output of both approaches into a Meta Classifier so that its output specifies the final spam detection or the normality of the tweet text. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets. Manuscript profile
Open Access Article
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3 - Improving polarity identification in sentiment analysis using sarcasm detection and machine learning algorithms in Persian tweets
Shaghayegh hajiabdollah Mitra Mirzarezaee Mir Mohsen Pedram
20.1001.1.27170411.1401.14.53.11.1

Sentiment analysis is a branch of computer science and natural language processing that seeks to familiarize machines with human emotions and make them recognizable. Both sentiment analysis and sarcasm which is a sub-field of the former, seek to correctly identify the h More

Sentiment analysis is a branch of computer science and natural language processing that seeks to familiarize machines with human emotions and make them recognizable. Both sentiment analysis and sarcasm which is a sub-field of the former, seek to correctly identify the hidden positive and negative emotions of the text. The use of sarcasm on social media, where criticism can be exercised within the context of humor, is quite common. Detection of sarcasm has a special effect on correctly recognizing the polarization of an opinion, and thus not only it can help the machine to understand the text better, but also makes it possible for the respective author to get his message across more clearly. For this purpose, 8000 Persian tweets that have emotional labels and examined for the presence or absence of sarcasm have been used. The innovation of this research is in extracting keywords from sarcastic sentences. In this research, a separate classifier has been trained to identify irony of the text. The output of this classifier is provided as an added feature to the text recognition classifier. In addition to other keywords extracted from the text, emoticons and hashtags have also been used as features. Naive Bayes, support vector machines, and neural networks were used as baseline classifiers, and finally the combination of classifiers was used to identify the feeling of the text. The results of this study show that identifying the irony in the text and using it to identify emotions increases the accuracy of the results. Manuscript profile

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List of Articles Ensemble Learning