Text Document Classification basedon Least Square Support Vector Machines with Singular Value Decomposition

M.Ramakrishna, Murty; J.V.R, Murthy; Prasad, Reddy P.V.G.D

doi:10.5120/3312-4540

Cited by 31 publications

(2 citation statements)

References 10 publications

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“…Optimal dimension reduction is the main objective of SVD. The various data types applied on SVD from literature search are Text [62] [63], Image [64] [65], Audio [66] [67], video [68] [69], Text [62] [63], Times series [70] [109] [71] and Structured data [72] [73].…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Review of Dimension Reduction Methods

Nanga¹,

Bawah²,

Acquaye³

et al. 2021

JDAIP

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Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR tech

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Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Review of Dimension Reduction Methods

Nanga¹,

Bawah²,

Acquaye³

et al. 2021

JDAIP

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“…Categorization or classification of text is considered to be one of the important topics in the field of natural language processing (NLP); it is also an essential tool in diverse fields such as filtering information, categorization of topics, searching the web, and sentimental analysis [1]. One of the simple ways of explaining text classification is described as follows: Given a group of documents containing a group of classes, a function is defined that will assign a value to the group of classes for each document [2]. Text classification or text mining approaches are used to extract important information from a large amount of text data in a short time [3].…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based model for web-based text classification

Aich

Chakraborty

Kim

2019

IJECE

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<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different topics at different levels, which may adversely affect the accuracy of traditional approaches. With the invention of deep learning models, which already have the capability of providing good accuracy in the field of image processing and speech recognition, the problems inherent in the traditional text classification model can be overcome. Currently, there are several deep learning models such as a convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory that are widely used for various text-related tasks; however, among them, the CNN model is popular because it is simple to use and has high accuracy for text classification. In this study, classification of random texts on the web into categories is attempted using a CNN-based model by changing the hyperparameters and sequence of text vectors. We attempt to tune every hyperparameter that is unique for the classification task along with the sequences of word vectors to obtain the desired accuracy; the accuracy is found to be in the range of 85–92%. This model can be considered as a reliable model and applied to solve real-world problem or extract useful information for various text mining applications.</p></td></tr></tbody></table>

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