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Relevant bibliographies by topics / Random Forests Classifiers

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Academic literature on the topic 'Random Forests Classifiers'

Author: Grafiati

Published: 28 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Random Forests Classifiers"

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Sadorsky, Perry. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers." Journal of Risk and Financial Management 14, no. 5 (2021): 198. http://dx.doi.org/10.3390/jrfm14050198.

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Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stocha

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Kulyukin, Vladimir, Nikhil Ganta, and Anastasiia Tkachenko. "On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks." Applied Sciences 11, no. 17 (2021): 8141. http://dx.doi.org/10.3390/app11178141.

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Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time. Automated video analysis of such traffic is critical for continuous colony health assessment. In our previous research, we proposed a two-tier algorithm to measure omnidirectional bee traffic in videos. Our algorithm combines motion detection with image classification: in tier 1, motion detection functions as class-agnostic object location to generate regions with possible objects; in tier 2, each region from tier 1 is classified by a c

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Daho, Mostafa El Habib, and Mohammed Amine Chikh. "Combining Bootstrapping Samples, Random Subspaces and Random Forests to Build Classifiers." Journal of Medical Imaging and Health Informatics 5, no. 3 (2015): 539–44. http://dx.doi.org/10.1166/jmihi.2015.1423.

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Alhudhaif, Adi. "A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach." PeerJ Computer Science 7 (May 14, 2021): e523. http://dx.doi.org/10.7717/peerj-cs.523.

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Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals.

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Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (2020): 158. http://dx.doi.org/10.3390/info11030158.

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Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests an

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Polaka, Inese, Igor Tom, and Arkady Borisov. "Decision Tree Classifiers in Bioinformatics." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 118–23. http://dx.doi.org/10.2478/v10143-010-0052-4.

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Decision Tree Classifiers in BioinformaticsThis paper presents a literature review of articles related to the use of decision tree classifiers in gene microarray data analysis published in the last ten years. The main focus is on researches solving the cancer classification problem using single decision tree classifiers (algorithms C4.5 and CART) and decision tree forests (e.g. random forests) showing strengths and weaknesses of the proposed methodologies when compared to other popular classification methods. The article also touches the use of decision tree classifiers in gene selection.

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El Habib Daho, Mostafa, Nesma Settouti, Mohammed El Amine Bechar, Amina Boublenza, and Mohammed Amine Chikh. "A new correlation-based approach for ensemble selection in random forests." International Journal of Intelligent Computing and Cybernetics 14, no. 2 (2021): 251–68. http://dx.doi.org/10.1108/ijicc-10-2020-0147.

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PurposeEnsemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance.Design/methodology/approachIn this work, the authors are proposing a pruning method that takes into consideration the correla

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Krautenbacher, Norbert, Fabian J. Theis, and Christiane Fuchs. "Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies." Computational and Mathematical Methods in Medicine 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7847531.

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Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random for

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Liu, Sheng, Yixin Chen, and Dawn Wilkins. "Large margin classifiers and Random Forests for integrated biological prediction." International Journal of Bioinformatics Research and Applications 8, no. 1/2 (2012): 38. http://dx.doi.org/10.1504/ijbra.2012.045975.

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Van Assche, Anneleen, Celine Vens, Hendrik Blockeel, and Sašo Džeroski. "First order random forests: Learning relational classifiers with complex aggregates." Machine Learning 64, no. 1-3 (2006): 149–82. http://dx.doi.org/10.1007/s10994-006-8713-9.

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Dissertations / Theses on the topic "Random Forests Classifiers"

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Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 53).<br>The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely re

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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained thr

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Sandsveden, Daniel. "Evaluation of Random Forests for Detection and Localization of Cattle Eyes." Thesis, Linköpings universitet, Datorseende, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540.

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In a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to discover diseases is to use thermal images and automatic head and eye detectors. In this thesis an eye detector and a head detector is implemented using the Random Forests classifier. During the implementation the classifier is evaluated using three different descriptors: Histogram of Oriented Gradients, Local Binary Patterns, and a descriptor based on pixel differences. An alternative classifier, the Support Vector Machine, is also evaluated for comp

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Abd, El Meguid Mostafa. "Unconstrained facial expression recognition in still images and video sequences using Random Forest classifiers." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107692.

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The aim of this project is to construct and implement a comprehensive facial expression detection and classification framework through the use of a proprietary face detector (PittPatt) and a novel classifier consisting of a set of Random Forests paired with either support vector machine or k-nearest neighbour labellers. The system should perform at real-time rates under unconstrained image conditions, with no intermediate human intervention. The still-image Binghamton University 3D Facial Expression database was used for training purposes, while a number of other expression-labelled video data

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Sjöqvist, Hugo. "Classifying Forest Cover type with cartographic variables via the Support Vector Machine, Naive Bayes and Random Forest classifiers." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-58384.

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Halmann, Marju. "Email Mining Classifier : The empirical study on combining the topic modelling with Random Forest classification." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14710.

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Filtering out and replying automatically to emails are of interest to many but is hard due to the complexity of the language and to dependencies of background information that is not present in the email itself. This paper investigates whether Latent Dirichlet Allocation (LDA) combined with Random Forest classifier can be used for the more general email classification task and how it compares to other existing email classifiers. The comparison is based on the literature study and on the empirical experimentation using two real-life datasets. Firstly, a literature study is performed to gain ins

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Zhang, Qing Frankowski Ralph. "An empirical evaluation of the random forests classifier models for variable selection in a large-scale lung cancer case-control study /." See options below, 2006. http://proquest.umi.com/pqdweb?did=1324365481&sid=1&Fmt=2&clientId=68716&RQT=309&VName=PQD.

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Xia, Junshi. "Multiple classifier systems for the classification of hyperspectral data." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT047/document.

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Dans cette thèse, nous proposons plusieurs nouvelles techniques pour la classification d'images hyperspectrales basées sur l'apprentissage d'ensemble. Le cadre proposé introduit des innovations importantes par rapport aux approches précédentes dans le même domaine, dont beaucoup sont basées principalement sur un algorithme individuel. Tout d'abord, nous proposons d'utiliser la Forêt de Rotation (Rotation Forest) avec différentes techiniques d'extraction de caractéristiques linéaire et nous comparons nos méthodes avec les approches d'ensemble traditionnelles, tels que Bagging, Boosting, Sous-es

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Pettersson, Anders. "High-Dimensional Classification Models with Applications to Email Targeting." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168203.

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Email communication is valuable for any modern company, since it offers an easy mean for spreading important information or advertising new products, features or offers and much more. To be able to identify which customers that would be interested in certain information would make it possible to significantly improve a company's email communication and as such avoiding that customers start ignoring messages and creating unnecessary badwill. This thesis focuses on trying to target customers by applying statistical learning methods to historical data provided by the music streaming company Spoti

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Amlathe, Prakhar. "Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7050.

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Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation. Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony beha

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Book chapters on the topic "Random Forests Classifiers"

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Latinne, Patrice, Olivier Debeir, and Christine Decaestecker. "Limiting the Number of Trees in Random Forests." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_18.

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Bernard, Simon, Laurent Heutte, and Sébastien Adam. "Influence of Hyperparameters on Random Forest Accuracy." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_18.

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Baumann, Florian, Fangda Li, Arne Ehlers, and Bodo Rosenhahn. "Thresholding a Random Forest Classifier." In Advances in Visual Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14364-4_10.

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Smith, R. S., M. Bober, and T. Windeatt. "A Comparison of Random Forest with ECOC-Based Classifiers." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21557-5_23.

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Svetnik, Vladimir, Andy Liaw, Christopher Tong, and Ting Wang. "Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25966-4_33.

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Mishra, Sushruta, Yeshihareg Tadesse, Anuttam Dash, Lambodar Jena, and Piyush Ranjan. "Thyroid Disorder Analysis Using Random Forest Classifier." In Smart Innovation, Systems and Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_39.

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Tiwari, Kamlesh, and Mayank Patel. "Facial Expression Recognition Using Random Forest Classifier." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1059-5_15.

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Vakharia, V., S. Vaishnani, and H. Thakker. "Appliances Energy Prediction Using Random Forest Classifier." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8704-7_50.

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Zhang, Wenbin, Albert Bifet, Xiangliang Zhang, Jeremy C. Weiss, and Wolfgang Nejdl. "FARF: A Fair and Adaptive Random Forests Classifier." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_20.

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Camgöz, Necati Cihan, Ahmet Alp Kindiroglu, and Lale Akarun. "Gesture Recognition Using Template Based Random Forest Classifiers." In Computer Vision - ECCV 2014 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16178-5_41.

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Conference papers on the topic "Random Forests Classifiers"

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Izza, Yacine, and Joao Marques-Silva. "On Explaining Random Forests with SAT." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/356.

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Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for computing explanations of RFs. Moreover, there is recent work on polynomial algorithms for explaining ML models, including naive Bayes classifiers. Hence, one question is whether finding explanations of RFs can be solved in polynomial time. This paper answers this question negatively, by proving that computing one PI-explanation of an RF is D^P-hard. Furthermore, the paper proposes a propositional encoding for computing

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Sathe, Saket, and Charu C. Aggarwal. "Nearest Neighbor Classifiers Versus Random Forests and Support Vector Machines." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00164.

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Cohen, Joseph, Baoyang Jiang, and Jun Ni. "Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8360.

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Abstract Especially common in discrete manufacturing, timed event systems often require a high degree of synchronization for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the norm

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"Ensemble Learning Approach for Clickbait Detection Using Article Headline Features." In InSITE 2019: Informing Science + IT Education Conferences: Jerusalem. Informing Science Institute, 2019. http://dx.doi.org/10.28945/4319.

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[This Proceedings paper was revised and published in the 2019 issue of the journal Informing Science: The International Journal of an Emerging Transdiscipline, Volume 22] Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread

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Losi, Enzo, Mauro Venturini, Lucrezia Manservigi, et al. "Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-58916.

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Abstract A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble

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J. Stein, Aviel, Janith Weerasinghe, Spiros Mancoridis, and Rachel Greenstadt. "News Article Text Classification and Summary for Authors and Topics." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101401.

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News articles are important for providing timely, historic information. However, the Internet is replete with text that may contain irrelevant or unhelpful information, therefore means of processing it and distilling content is important and useful to human readers as well as information extracting tools. Some common questions we may want to answer are “what is this article about?” and “who wrote it?”. In this work we compare machine learning models for evaluating two common NLP tasks, topic and authorship attribution, on the 2017 Vox Media dataset. Additionally, we use the models to classify

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Das, Dipankar, and Krishna Sharma. "Leveraging of Weighted Ensemble Technique for Identifying Medical Concepts from Clinical Texts at Word and Phrase Level." In 2nd International Conference on Machine Learning, IOT and Blockchain (MLIOB 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111213.

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Concept identification from medical texts becomes important due to digitization. However, it is not always feasible to identify all such medical concepts manually. Thus, in the present attempt, we have applied five machine learning classifiers (Support Vector Machine, K-Nearest Neighbours, Logistic Regression, Random Forest and Naïve Bayes) and one deep learning classifier (Long Short Term Memory) to identify medical concepts by training a total of 27.383K sentences. In addition, we have also developed a rule based phrase identification module to help the existing classifiers for identifying m

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Schnebly, James, and Shamik Sengupta. "Random Forest Twitter Bot Classifier." In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2019. http://dx.doi.org/10.1109/ccwc.2019.8666593.

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Kocher, Geeta, and Gulshan Kumar. "Performance Analysis of Machine Learning Classifiers for Intrusion Detection using UNSW-NB15 Dataset." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102004.

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With the advancement of internet technology, the numbers of threats are also rising exponentially. To reduce the impact of these threats, researchers have proposed many solutions for intrusion detection. In the literature, various machine learning classifiers are trained on older datasets for intrusion detection which limits their detection accuracy. So, there is a need to train the machine learning classifiers on latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. On the basis of theoretical analysis, taxonomy is proposed in terms of laz

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Mohandoss, Divya Pramasani, Yong Shi, and Kun Suo. "Outlier Prediction Using Random Forest Classifier." In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2021. http://dx.doi.org/10.1109/ccwc51732.2021.9376077.

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