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Empirical evaluations of preprocessing parameters' impact on predictive coding's effectiveness

Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang, Haozhen Zhao
2016 2016 IEEE International Conference on Big Data (Big Data)  
Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model.  ...  Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts.  ...  MACHINE LEARNING ALGORITHMS We conducted experiments to compare two popular machine learning algorithms, Support Vector Machine (SVM) and Logistic Regression (LR).  ... 
doi:10.1109/bigdata.2016.7840747 dblp:conf/bigdataconf/ChhatwalHKZZ16 fatcat:pluhxa4xdbh5tcdmhzrwcq4ls4

Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil (Preprint)

Íris Viana dos Santos Santana, Andressa C. M. da Silveira, Álvaro Sobrinho, Lenardo Chaves e Silva, Leandro Dias da Silva, Danilo Freire de Souza Santos, Edmar Candeia, Angelo Perkusich
2021 Journal of Medical Internet Research  
Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR).  ...  this study is to effectively prioritize symptomatic patients for testing to assist the early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. raw data  ...  The data sets generated or analyzed during this study are available in the data set of Brazilian patients who were symptomatic for screening the risk of COVID-19 [10] .  ... 
doi:10.2196/27293 pmid:33750734 fatcat:l43ylojjhva4flraswnytg2cji


Zetta Nillawati Reyka Putri, Muhammad Muhajir
2021 Jurnal Riset Informatika  
The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method.  ...  Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage.  ...  Support Vector Machine Support Vector Machines (SVM) is a data algorithm that is growing in popularity in the machine learning community (Yang et al., 2013) .  ... 
doi:10.34288/jri.v4i1.262 fatcat:alqcniyq2rdhxenllzvuhv64lm

Empirical Process Monitoring Via Chemometric Analysis Of Partially Unbalanced Data

Hyun-Woo Cho
2013 Zenodo  
Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data.  ...  This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data.  ...  The unbalanced data problem of batch processes was solved by the use of the support vector data description technique.  ... 
doi:10.5281/zenodo.1088942 fatcat:y4fonrxhzjg7no53ksdy2u26oa

Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages

Thomas Renault
2019 Digital Finance  
We use a large dataset of one million messages sent on the microblogging platform StockTwits to evaluate the performance of a wide range of preprocessing methods and machine learning algorithms for sentiment  ...  However, more complex and time-consuming machine learning methods, such as random forests or neural networks, do not improve the accuracy of the classification.  ...  Vector Machine.  ... 
doi:10.1007/s42521-019-00014-x fatcat:olpp2fyntre75mlusm7k33geve

Sentiment Analysis in English Texts

Arwa Alshamsi, Reem Bayari, Said Salloum
2020 Advances in Science, Technology and Engineering Systems  
This research paper explored text classification accuracy while using different classifiers for classifying balanced and unbalanced datasets.  ...  This research paper aims to obtain a dataset of tweets and apply different machine learning algorithms to analyze and classify texts.  ...  The authors applied algorithms to predict DJIA and S&P500 indicators using Support Vectors Machine (SVM) and Neural Networks (NN).  ... 
doi:10.25046/aj0506200 fatcat:6qy52jd5zvc4tdb4dzhrhvrgzm

Vibration Analysis in Turbomachines Using Machine Learning Techniques

Allan Alves Pinheiro, Iago Modesto Brandao, Cesar Da Costa
2019 European Journal of Engineering Research and Science  
Axis unbalance defect is classified using support vector machines.  ...  In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance.  ...  Axis unbalance fault is classified using support vector machines.  ... 
doi:10.24018/ejers.2019.4.2.1128 fatcat:oxsj2twz2fdqbophtfbvedhij4

Effective predictive modelling for coronary artery diseases using support vector machine

Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Angga Vidianto, Wayan Firdaus Mahmudy
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
Hyperparameter tuning is also done to find the best combination of parameters in support vector machines (SVM).  ...  This study focuses on effective modeling capable of predicting CAD using feature selection to handle high dimensional data and feature resampling to handle unbalanced data.  ...  Int J Artif Intell ISSN: 2252-8938 Effective predictive modelling for coronary artery diseases using support … (Kuncahyo Setyo Nugroho) 349 Support vector machine The SVM is a highly effective classification  ... 
doi:10.11591/ijai.v11.i1.pp345-355 fatcat:ukjo4uksbjg3ddo72lsfqcsnme

Review of classification methods on unbalanced data sets

Le Wang, Meng Han, Xiaojuan Li, Ni Zhang, Haodong Cheng
2021 IEEE Access  
first kind of support vector machines.  ...  [40] used a single-class support vector machine to detect the imbalance between normal data and abnormal data.  ... 
doi:10.1109/access.2021.3074243 fatcat:52royfjis5htbgbzawdjamtnky

Distribution-Sensitive Learning on Relevance Vector Machine for Pose-Based Human Gesture Recognition

Vina Ayumi, Mohamad Ivan Fanany
2015 Procedia Computer Science  
., Relevance Vector Machine (RVM), to deal with the imbalanced data problem. This prior analyzes the training dataset before learning a model.  ...  It is often impractical to reacquire the data or to modify the existing dataset using oversampling or undersampling procedures.  ...  Lin and Wang proposed a Fuzzy Support Vector Machine (FSVM) to eliminate effects caused by the unbalanced data and noise [11] .  ... 
doi:10.1016/j.procs.2015.12.160 fatcat:6maan4btzfhixewb6qq5hpmmqm

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Ruben Ruiz-Gonzalez, Jaime Gomez-Gil, Francisco Gomez-Gil, Víctor Martínez-Martínez
2014 Sensors  
To do so, a Support Vector Machine (SVM)-based system is employed.  ...  Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features.  ...  Support Vector Machines for Classification Support Vector Machines (SVM) is a statistical supervised machine learning technique, used both for classification and for regression purposes.  ... 
doi:10.3390/s141120713 pmid:25372618 pmcid:PMC4279508 fatcat:sv427dbylfdvxkhbvodboh5wg4

Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

Muhamad Azhar, Noor Hafidz, Biktra Rudianto, Windu Gata
2020 PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic  
Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.  ...  Therefore, this paper analysis the customer's sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature  ...  Naive Bayes And Support Vector Machine (2020)Figure 1.  ... 
doi:10.33558/piksel.v8i2.2272 fatcat:sisi34ntonbd5amv45d2a7hmc4

Ensemble Method for Indonesian Twitter Hate Speech Detection

M. Ali Fauzi, Anny Yuniarti
2018 Indonesian Journal of Electrical Engineering and Computer Science  
We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and  ...  Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet.  ...  (RF) [13] , or Support Vector Machines (SVM) [1] for classification task.  ... 
doi:10.11591/ijeecs.v11.i1.pp294-299 fatcat:d72teuqss5dmfh35gznur2own4

A Proposed Hybrid Algorithm for detecting COVID-19 Patients

Alla Ahmad Hassan, Tarik A Rashid
2021 Kurdistan Journal of Applied Research  
Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian  ...  Machine learning techniques for detection and classification are commonly used in current medical diagnoses.  ...  Vector Machine, BF Tree algorithms using Both unbalanced/balanced cross-validation of 10-fold and testing set are 0.  ... 
doi:10.24017/science.2021.2.5 fatcat:3knnksyyfzcdzini45uka4p3hq

Arabic Authorship Attribution Using Synthetic Minority Over-Sampling Technique and Principal Components Analysis for Imbalanced Documents

Hassina Hadjadj, Halim Sayoud
2021 International Journal of Cognitive Informatics and Natural Intelligence  
Nowadays, dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task.  ...  The used dataset contains 7 Arabic books written by 7 different scholars, which are segmented into text segments of the same size, with an average length of 2900 words per text.  ...  The nearest data point to the margin is known as support vector.  ... 
doi:10.4018/ijcini.20211001.oa33 fatcat:uxonzf2v2ndnpeuhi3p7lcetm4
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