Performance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiers

Başarslan, Muhammet Sinan; Kayaalp, Fatih

doi:10.1109/ebbt.2019.8741688

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…, x(L)] 𝑇 ∈ 𝑟 𝐿 ) belongs to class 𝑆 İ When the independence proposition is used statistically in the Bayes decision theorem, this type of classification is called NB classification. In a mathematical expression (Equation 1) [22].…”

Section: Naïve Bayesmentioning

confidence: 99%

“…(3) P(S i ) and P(S j ) is the prior probability of classes I and j. Their values can be easily calculated from the studied data set [22]. The NB classifier is used in many fields in artificial intelligence studies, including disease diagnosis [23][24].…”

Section: 𝑃(𝑥|𝑆 𝑖 )𝑃(𝑆 𝑖 ) > 𝑃(𝑥|𝑆 𝑗 )𝑃(𝑆 𝑗 ) ∀𝑗 ≠ 𝑖mentioning

confidence: 99%

“…In the hyperplane determines in which class the new sample will be placed [25]. SVM excel at both linear and nonlinear classification tasks [22].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Logistic regression uses the "maximum likelihood" method. Logistic regression uses the sigmoid function to classify, the sigmoid function is an "S" shaped curve [22].…”

Section: Logistic Regressionmentioning

confidence: 99%

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Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

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Section: Naïve Bayesmentioning

confidence: 99%

Section: 𝑃(𝑥|𝑆 𝑖 )𝑃(𝑆 𝑖 ) > 𝑃(𝑥|𝑆 𝑗 )𝑃(𝑆 𝑗 ) ∀𝑗 ≠ 𝑖mentioning

confidence: 99%

“…In the hyperplane determines in which class the new sample will be placed [25]. SVM excel at both linear and nonlinear classification tasks [22].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Logistic regression uses the "maximum likelihood" method. Logistic regression uses the sigmoid function to classify, the sigmoid function is an "S" shaped curve [22].…”

Section: Logistic Regressionmentioning

confidence: 99%

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Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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“…Although CNN is one of the deep learning algorithms used in artificial intelligence fields such as Natural Language processing, it is also often used in the field of Image processing. It consists of three main layers [35]:…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Başarslan

Kayaalp

2021

Sakarya University Journal of Computer and Information Sciences

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Thanks to social media, people are now able to leave guiding comments quickly about their favorite restaurants, movies, etc. This has paved the way for the field of sentiment analysis, which brings together various disciplines. In this study, Yelp restaurant reviews and IMDB movie reviews dataset were used together with the data collected from Twitter. Word2Vec (W2V), Global Vector (GloVe) and Bidirectional Encoder Representation (BERT) word embedding methods, Term Frequency-Reverse Document Frequency (TF-IDF), and the Bag-of-Words (BOW) were used on these datasets. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB) were used in the sentiment analysis models. Accuracy, F-measure (F), Sensitivity (Sens), Precision (Pre), and Receiver Operating Characteristics (ROC) were used in the evaluation of the model performance. The Accuracy rates of the models created by the Machine Learning (ML) and Deep Learning (DL) methods using the IMDB dataset were in the range of 81%-90% and 84%-94%, respectively. These rates were in the range of 80%-86% and 81%-89% for the Yelp dataset, and in the range of 75%-79% and 85%-98% for the Twitter dataset. The models that incorporated the BERT word embedding method have the best performance, compared to the other models with ML and DL. Therefore, BERT method is recommended for this type of analysis in future studies.

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Sentiment Analysis with Various Deep Learning Models on Movie Reviews

Başarslan

Kayaalp

2022

2022 International Conference on Artificial Intelligence of Things (ICAIoT)

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Performance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiers

Cited by 6 publications

References 9 publications

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Sentiment Analysis with Various Deep Learning Models on Movie Reviews

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