![]() ![]() Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Jani K, Chaudhuri M, Patel H, Shah M (2019) Machine learning in films: an approach towards automation in film censoring. Hmeidi I, Hawashin B, El-Qawasmeh E (2008) Performance of KNN and SVM classifiers on full word Arabic articles. Genkin A, Lewis DD, Madigan D (2007) Large-scale Bayesian logistic regression for text categorization. Garla V, Taylor C, Brandt C (2013) Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management. Gandhi M, Kamdar J, Shah M (2020) Preprocessing of Non-symmetrical images for edge detection. In: IEEE/ACM 40th international conference on software engineering: companion (ICSE-Companion), Gothenburg, pp 536–537 įerrari A (2018) Natural language requirements processing: from research to practice. J Inf Optim Sci 39(4):973–987Įlghazel H, Aussem A, Gharroudi O, Saadaoui W (2016) Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Syst Appl 36(3–1):5432–5435Ĭheng Y, Rui K (2017) Text classification of minimal risk with three-way decisions. Ĭhen J, Huang H, Tian S, Qu Y (2009) Feature selection for text classification with Naïve Bayes. Mach Learn 45(1):5–32Ĭhatzigeorgakidis G, Karagiorgou S, Athanasiou S, Skiadopoulos S (2018) FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins. Springer, Chamīreiman L (2001) Random forests. Lecture notes in computer science, vol 8646. In: Bellatreche L, Mohania MK (eds) Data warehousing and knowledge discovery. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT), Chennai, pp 61–66īouaziz A, Dartigues-Pallez C, da Costa Pereira C, Precioso F, Lloret P (2014) Short text classification using semantic random forest. īafna P, Pramod D, Vaidya A (2016) Document clustering: TF-IDF approach. Īydoğan M, Karci A (2019) Improving the accuracy using pre-trained word embedding on deep neural networks for Turkish text classification. Inf Process Manag 54(6):1129–1153Īseervatham S, Antoniadis A, Gaussier E, Burlet M, Denneulin Y (2011) A sparse version of the ridge logistic regression for large-scale text categorization. ![]() Proc Comput Sci 127:511–520Īltınel B, Ganiz MC (2018) Semantic text classification: a survey of past and recent advances. Augment Hum Res 5:7Īl Amrani Y, Lazaar M, El Kadiri KE (2018) Random forest and support vector machine based hybrid approach to sentiment analysis. The classifier which gets the highest among all these parameters is termed as the best machine learning algorithm for the BBC news data set.Īhir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality for enhanced education learning, military training and sports. The authors decided to show the comparison based on five parameters namely precision, accuracy, F1-score, support and confusion matrix. The experimental conclusion shows that BBC news text classification model gets satisfying results on the basis of algorithms tested on the data set. Then, these classifiers were tested, analysed and compared with each other and finally got a conclusion. In the classifier implementation section, the authors separately chose and compared logistic regression, random forest and K-nearest neighbour as our classification algorithms. In this paper, a BBC news text classification system is designed. Based on different machine learning algorithms used in the current paper, the system of text classification is divided into four sections namely text pre-treatment, text representation, implementation of the classifier and classification. The classes are then classified by determining the text types of the content. The key technology for gaining the insights into a text information and organizing that information is known as text classification. In the current generation, a huge amount of textual documents are generated and there is an urgent need to organize them in a proper structure so that classification can be performed and categories can be properly defined. ![]()
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