A Brief Survey on Text Classification Using Various Machine Learning Techniques

Padmavathi .S, M. Chidambaram

Abstract


Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Full Text:

PDF

References


C.L.Liu, W.H.Hsaio, C.H.Lee, T.H.Chang, T.H.Kuo, “Semi-Supervised Text Classification with Universum Learning”, IEEE Trans. on Cybernetics, Vol.46, Issue.2, P.462– 473, 2016.

J.Peng, A.J.Aved, G.Seetharaman, K.Palaniappan, “Multiview Boosting With Information Propagation for Classification”, IEEE Trans. on Neural Networks and Learning Systems, 2017.

F.Zhuang, P.Luo, C.Du, Q.He, Z.Shi, H.Xiong, “Triplex Transfer Learning: Exploiting Both Shared and Distinct Concepts for Text Classification” IEEE Trans. on Cybernetics, Vol.44, Issue.7, PP.1191–1203, 2014.

R.C.P.Fragoso, R.H.W.Pinheiro, G.D.C.Cavalcanti, “A method for automatic determination of the feature vector size for text categorization”, 5th Brazilian Con. on Intelligent Systems, 2016.

J.A.Otaibi, Z.Safi, A.Hassaine, F.Islam and A.jaoua, “Machine Learning and Conceptual Reasoning for Inconsistency Detection”, IEEE Access, 2017, Vol.5, PP.338–346.

S.Baccianella, A.Esuli, F.Sebastiani, “Feature Selection for Ordinal Text Classification”, Neural Computation, Vol.26, Issue.3, Pages.557–591, 2014.

B.Zhang, A.Marin, B.Hutchinson, M.Ostendorf, “Learning Phrase Patterns for Text Classification”, IEEE Transactions on Audio, Speech, and Language Processing, Vol.21, Issue.6, PP.1180–1189, 2013.

J.Y.Jiang, R.J.Liou, S.J.Lee, “A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification”, IEEE Transactions on Knowledge and Data Engineering, Vol.23, Issue.3, PP.335 – 349, 2011.

C.Silva, U.Lotric, B.Ribeiro, A.Dobnikar, “Distributed Text Classification With an Ensemble Kernel-Based Learning Approach”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) Vol.40, Issue.3, PP.287 – 297, 2010.

Y.Tang, & X.Wu, “Scene Text Detection and Segmentation based on Cascaded Convolution Neural Networks”, IEEE Transactions on Image Processing, 2017, Vol.26, Issue.3, PP.1509 – 1520.

M.Elhoseiny, A.Elgammal & B.Saleh, “Write a Classifier: Predicting Visual Classifiers from Unstructured Text”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017, Vol.39, Issue.12, PP.2539 – 2553.

C.Silberer, V.Ferrari & M.Lapata, “Visually Grounded Meaning Representations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, Vol.39, Issue.11, PP.2284 – 2297.

M.Morchid, M.Bouallegue, R.Dufour, G.Linarès, D.Matrouf & R.D.Mori, “Compact Multiview Representation of Documents Based on the Total Variability Space”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Year: 2015, Volume: 23, Issue: 8, Pages: 1295 – 1308.




DOI: https://doi.org/10.23956/ijarcsse.v8i1.521

Refbacks

  • There are currently no refbacks.




© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.