Ensemble of keyword extraction methods and classifiers in text classification

Onan, Aytuğ; Korukoğlu, Serdar; Bulut, Hasan

doi:10.1016/j.eswa.2016.03.045

Cited by 564 publications

(317 citation statements)

References 35 publications

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“…For training and testing data are split randomly. of false negatives, the number of false positives, the number of true positives and the number of true negatives, respectively [29].…”

Section: Classification With Statistical Learning Methodsmentioning

confidence: 99%

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.

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“…For training and testing data are split randomly. of false negatives, the number of false positives, the number of true positives and the number of true negatives, respectively [29].…”

Section: Classification With Statistical Learning Methodsmentioning

confidence: 99%

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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“…In a recent study [15], Onan et al empirically evaluate the predictive performance of ensemble learning methods on text documents that are represented keywords. They first apply different keyword extraction algorithms to test dataset.…”

Section: Related Workmentioning

confidence: 99%

The effectiveness of homogenous ensemble classifiers for Turkish and English texts

Kilimci

Akyokuş

Omurca

2016

2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

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Abstract-Text categorization has become more and more popular and important problem day by day because of the large proliferation of documents in many fields. To come up with this problem, several machine learning techniques are used for categorization such as naïve Bayes, support vector machines, artificial neural networks, etc. In this study, we concentrate on ensemble of multiple classifiers instead of using only a single one. We perform a comparative analysis of the impact of the ensemble techniques for text categorization domain. To carry out this, the same type of base classifiers but diversified training sets are used which is referred as homogenous ensembles. In order to diversify the training dataset, various ensemble algorithms are utilized such as Bagging, Boosting, Random Subspace and Random Forest. Multivariate Bernoulli Naïve Bayes is preferred as a base classifier due to its superior classification performance compared to the success of the other single classifiers. A wide range of comparative and extensive empirical studies are conducted on four widely-used datasets in text categorization domain in both Turkish and English. Finally, the effectiveness of ensemble algorithms is discussed.

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“…For this reason, [18] performed an empirical analysis on statistical methods for the extraction of keywords using the ACM (Association for Computing Machinery) and Reuters-21578 document collections. Authors also described the predictive behavior of classification algorithms and joint learning methods when using keywords to represent scientific text documents, thus demonstrating that as the number of keywords increases, the predictive performance of the classifiers tends to increase too.…”

Section: Classification Techniquesmentioning

confidence: 99%

“…Nevertheless, to the best of our knowledge, there are not works which used association rule mining and Bayesian networks to analyze the decrease in the number of autopsies performed in a hospital; therefore Scientific Programming 5 [12] Bayesian networks Classification [13] Logistic regression, NB Classification [14] Re-RX, J48graft Classification [15] NB, SVM Classification [16] NB, SVM, RF Classification [17] J48, RF, KNN, NB, SVM Classification [18] NB, SVM, logistic regression, RF Classification [19] NB, OTM, InterVA-4 Classification [21] Decision tree, Neural Networks Classification [22] Association rules Association [23] Apriori Association [24] Fuzzy association rules Mining and fuzzy logic Association [25] Formal Concept Analysis Association…”

Section: Association Rule Mining Given the Variety Of Traditionalmentioning

confidence: 99%

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

Delgado

Rodríguez-Mazahua

Guzmán³

et al. 2018

Scientific Programming

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This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.

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Ensemble of keyword extraction methods and classifiers in text classification

Cited by 564 publications

References 35 publications

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

The effectiveness of homogenous ensemble classifiers for Turkish and English texts

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

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