Performance analysis of supervised machine learning algorithms for text classification

Mishu, Sadia Zaman; Rafiuddin, S M

doi:10.1109/iccitechn.2016.7860233

Cited by 26 publications

(8 citation statements)

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“…The neural process of posterior diffusion [10] consists of three layers as shown in Figure 2, with good illustration of the presence of an input layer, an output layer and a hidden layer of data. It is training in two distinct ways: forward propagation deep learning method and back deep learning process [24].…”

Section: Methodsmentioning

confidence: 99%

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Data mining in web personalization using the blended deep learning model

Abed

Fadhil

Al-Yaseen

2020

IJEECS

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In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.

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Section: Methodsmentioning

confidence: 99%

“…Many other models are compare to the Back Propagation Network (BPN) to create an independent platform for disaggregated data and supervised text classification process [10]. Graph-primarily based semisupervised gaining knowledge of (SSL) has attracted brilliant attention over the past decade [11].…”

Section: Introductionmentioning

confidence: 99%

Data mining in web personalization using the blended deep learning model

Abed

Fadhil

Al-Yaseen

2020

IJEECS

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“…In order to predict whether a document D belong to class C if, D≥ 0. LinearSVC is good when the number of features are high and the number of training examples is small [12]…”

Section: Linear Svcmentioning

confidence: 99%

Machine Learning Based Text Document Classification for E-Learning

Thomas*,

2020

IJRTE

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The number of e-learning websites as well as e-contents are increasing exponentially over the years and most of the time it become cumbersome for a learner to find e-content suitable for learning as the learner gets overwhelmed by the enormity of the content availability. The proposed work focus on evaluating the efficiencies of the different classification algorithm for the identification of the e-learning content based on difficulty levels. The data is collected from many e-learning web sites through web scraping. The web scraper downloads the web pages and parse to text file. The text files were made to run through many machine learning classification algorithms to find out the best classification model suitable for achieving the highest score with minimum training and testing time. This method helps to understand the performance of different text classification algorithms on e-learning contents and identifies the classifier with high accuracy for document classification.

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“…Estes ajustes são feitos para aproximar a resposta real da rede da resposta desejada. Muitos autores (NAIK et al, 2015;MISHU;RAFIUDDIN, 2016;VINODHINE;CHANDRASEKARAN, 2016;PRASANNA;RAO, 2017) avaliaram o desempenho de algoritmos de classificação de textos que utilizavam RNA e ressaltaram o seu desempenho comparado aos métodos tradicionais.…”

Section: Redes Neurais Artificiaisunclassified

Avaliação das etapas de pré-processamento e de treinamento em algoritmos de classificação de textos no contexto da recuperação da informação

Guimarães

Meireles

Almeida

2019

Perspect. ciênc. inf.

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A quantidade de dados não estruturados cresce com a popularização da Internet. Textos em linguagem natural representam um conjunto relevante e significativo para análise e produção de conhecimento. Este trabalho propõe uma análise quantitativa das etapas de préprocessamento e de treinamento de um classificador de textos, que utiliza os sentimentos expressos pelos usuários como atributo. Para realização dos experimentos, foram utilizadas Redes Neurais Artificiais, como algoritmo classificador, e textos provenientes dos sites Amazon, IMDB e Yelp. A base textual permite análise da expressão de sentimentos positivos e negativos dos usuários em avaliações de produtos e serviços em textos não estruturados. Foram realizados dois processos distintos de pré-processamento e diferentes treinamentos das Redes Neurais Artificiais para classificação do

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Performance analysis of supervised machine learning algorithms for text classification

Cited by 26 publications

References 0 publications

Data mining in web personalization using the blended deep learning model

Data mining in web personalization using the blended deep learning model

Machine Learning Based Text Document Classification for E-Learning

Avaliação das etapas de pré-processamento e de treinamento em algoritmos de classificação de textos no contexto da recuperação da informação

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