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Collaborative work with linear classifier and extreme learning machine for fast text categorization

Wenbin Zheng, Hong Tang, Yuntao Qian
2013 World wide web (Bussum)  
This paper proposes a novel approach for fast text categorization, in which a collaborative work framework based on a linear classifier and an extreme learning machine (ELM) is constructed.  ...  The bloom of Internet has made fast text categorization very essential. Generally, the popular methods have good classification accuracy but slow speed, and vice versa.  ...  Acknowledgements The authors are grateful to anonymous reviewers for their constructive suggestions.  ... 
doi:10.1007/s11280-013-0225-5 fatcat:ktg6t6of4bgejkqdyvei7hyxhq

Comprehensive study and Analysis of Extreme Multi-Label Classification Approach

Purvi Prajapati
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Extreme Classification approach is the recently introduced research area to tackle large amount of data with multi-label environment for the classification.  ...  Extreme Multi-Label classifier will construct model and predict the relevant categories from the large amount of available categories.  ...  Collaborative Topic Regression (CTR) is used the text data for recommendation with probabilistic matrix factorization (PMF) [10, 20] .  ... 
doi:10.30534/ijatcse/2020/83922020 fatcat:qzgi7mtk4bfnbk4dmdjblp373y

JaTeCS an open-source JAva TExt Categorization System [article]

Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez
2017 arXiv   pre-print
resources, natural language processing tools, multi-language support, methods for feature selection and weighting, the implementation of many machine learning algorithms as well as wrappers for well-known  ...  As JaTeCS is focused on text as the main input data, it provides the user with many text-dedicated tools, e.g.: data readers for many formats, including the most commonly used text corpora and lexical  ...  These libraries provide optimized data representation and fast operations on dense/sparse arrays and matrices, together with efficient algorithms for linear algebra.  ... 
arXiv:1706.06802v1 fatcat:nxfaa5vsgvhrdarrrfczdhikuq

The Impact of Deep Learning Techniques on SMS Spam Filtering

Wael Hassan Gomaa
2020 International Journal of Advanced Computer Science and Applications  
classical machine learning.  ...  This paper explores the impact of applying various deep learning techniques on SMS spam filtering; by comparing the results of seven different deep neural network architectures and six classifiers for  ...  Classical Machine Learning Classifiers In this section we discuss briefly the six used machine learning classifiers: Naïve Bayes, Generalized Linear Model (GLM), Fast Large Margin, Decision Tree, Random  ... 
doi:10.14569/ijacsa.2020.0110167 fatcat:3eiy544pgzgdpcm5evdtxbqbsi

Feature Based Sentiment Analysis of Mobile Product Reviews using Machine Learning Techniques

Minu P Abraham
2020 International Journal of Advanced Trends in Computer Science and Engineering  
classify these opinions based on some machine learning algorithms.  ...  Hence, it is challenging for the online users, customers and manufacturers to make a proper decision about opinions on these reviews.  ...  Machine Learning Approaches The output generated by the algorithm Algo_Polarity is utilized for training and testing datasets to categorize the required opinions of the product reviews using machine learning  ... 
doi:10.30534/ijatcse/2020/210922020 fatcat:azpthajatfg3xj36ll3wnmv7h4

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjorn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen
2019 IEEE Access  
Our empirical comparisons with Naïve Bayes classifiers, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, are quite  ...  Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation.  ...  TEXT CATEGORIZATION WITH THE TSETLIN MACHINE In this section, we first present our Tsetlin Machine based framework for text categorization.  ... 
doi:10.1109/access.2019.2935416 fatcat:ocfgnpccbnfjvfnud73w5ils7q

Extreme Classification (Dagstuhl Seminar 18291)

Samy Bengio, Krzysztof Dembczynski, Thorsten Joachims, Marius Kloft, Manik Varma, Michael Wagner
2019 Dagstuhl Reports  
Extreme classification is a rapidly growing research area within machine learning focusing on multi-class and multi-label problems involving an extremely large number of labels (even more than a million  ...  Extreme classification has also opened up a new paradigm for key industrial applications such as ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked  ...  Text categorization with support vector machines: Learning with many relevant features. Springer, 1998. Nei Kato, Masato Suzuki, Shin Ichiro Omachi, Hirotomo Aso, and Yoshiaki Nemoto.  ... 
doi:10.4230/dagrep.8.7.62 dblp:journals/dagstuhl-reports/BengioDJKV18 fatcat:tglxen4d4vc5vkxtllzy3xokl4

Predictive Modeling Framework for Diabetes Classification Using Big Data Tools and Machine Learning

Jangam J. S. Mani, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
The Frame work has been carried based on extensive machine learning methods in association for processing of the data over spark RDD.  ...  The approach for the proposed system is much wider in term of predicting the diabetic model with enough feature variables explaining the patient historical data and diet habits.  ...  Conclusion The paper describes a big data frame work model associated with spark in order to predictive modeling and classifying the user diabetic or non-diabetic by employing the machine learning library  ... 
doi:10.17762/turcomat.v12i10.4255 fatcat:i25i5z6eurbdfeqvd3qgjll5xu

A Review on BCI Emotions Classification for EEG Signals Using Deep Learning [chapter]

Puja A. Chavan, Sharmishta Desai
2021 Advances in Parallel Computing  
This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems.  ...  In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms.  ...  Accuracy of proposed system using KNN and SVM algorithm The proposed system used KNN, SVM and CNN three different machine learning and deep learning classifiers which are evaluated with different cross-validation  ... 
doi:10.3233/apc210241 fatcat:dp4b5uv2ozf2zoaqttgf2pveui

An ELM-based model for affective analogical reasoning

Erik Cambria, Paolo Gastaldo, Federica Bisio, Rodolfo Zunino
2015 Neurocomputing  
In particular, by enabling a fast reconfiguration of such a vector space, extreme learning machines allow the polarity associated with natural language concepts to be calculated in a more dynamic and accurate  ...  In this paper, we explore how the high generalization performance, low computational complexity, and fast learning speed of extreme learning machines can be exploited to perform analogical reasoning in  ...  Therefore, in this work extreme learning machine (ELM) is adopted as a powerful tool to tackle this challenging task by exploiting inductive learning methodologies.  ... 
doi:10.1016/j.neucom.2014.01.064 fatcat:2edu4zyz2nd6bafltm7cglsjoy

A Survey on Resilient Machine Learning [article]

Atul Kumar, Sameep Mehta
2017 arXiv   pre-print
Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion  ...  All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs.  ...  This survey categorizes major works in Adversarial Machine Learning area in three broad categories.  ... 
arXiv:1707.03184v1 fatcat:qjylw7bvkzbdlbrof5cfpy2jyq

Prognosis of Diabetes Mellitus using Machine Learning Techniques

Vidya J, Swastika T Jain, Shyamala Boosi, Bhanujyothi H C, Dr.Chetana Tukkoji
2021 Turkish Journal of Computer and Mathematics Education  
More than 90% of people are diagnosed with Type 2 diabetes disease,T2D is a fast-growing, chronic disease caused by the imbalance in insulin function.  ...  Nowadays, the application of Machine learning in the medical field is gradually increasing.  ...  [9] M.Shanthi et al, have proposed a model for diagnosing T2D through ELM (Extreme Learning Machine) method.  ... 
doi:10.17762/turcomat.v12i5.1491 fatcat:hs63mhjeirc53ke35vbwphdcza

Detecting spam campaign in twitter with semantic similarity

M Mostafa, A Abdelwahab, H M Sayed
2020 Journal of Physics, Conference Series  
Experimental results show the ability of the proposed technique to extract the right candidate campaign and classify them as spam or not with high recall and precision.  ...  In this paper, the lightweight framework is proposed to take tweet text into consideration for optimizing spam campaign detection methods based on deep learning techniques.  ...  [25] use honeypots to obtain spam profile feature, which trained them with various machine learning algorithms, for instance LogitBoost and Decorate [14] .  ... 
doi:10.1088/1742-6596/1447/1/012044 fatcat:a73fmtla4ng7bpjotiqyicgjxe

Assessing Effectiveness of Exercised Variants of Machine Learning Techniques

Thus, the machine learning scheme is incorporated with deep learning and artificial intelligence technology.  ...  Also, this study evaluates the performance of various machine learning methods and their applications in different fields and also their limitations.  ...  machine and the linear classifier.  ... 
doi:10.35940/ijitee.d1781.029420 fatcat:3dig3j6ja5hovmazntsm3ldt3m

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Ayan Chatterjee, Martin W. Gerdes, Santiago G. Martinez
2020 Sensors  
Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight  ...  The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a  ...  Acknowledgments: Thanks to "University of Agder, Department of Information and Communication Technology, Center for eHealth" for giving me the infrastructure to carry out this research task and my supervisors  ... 
doi:10.3390/s20092734 pmid:32403349 fatcat:enxjacosmzcxhd3rxbht5qldtq
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