Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification

2021 ◽  
pp. 455-464
Author(s):  
Varun Dogra ◽  
Aman Singh ◽  
Sahil Verma ◽  
Kavita ◽  
N. Z. Jhanjhi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Dimensionality Reduction ◽  
Text Classification ◽  
Data Preprocessing ◽  
Feature Selection Techniques

Efficient n-gram construction for text categorization using feature selection techniques

2021 ◽  
Vol 25 (3) ◽  
pp. 509-525
Author(s):  
Maximiliano García ◽  
Sebastián Maldonado ◽  
Carla Vairetti
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Text Categorization ◽  
A Priori ◽  
Predictive Performance ◽  
Online Reviews ◽  
Additional Advantage ◽  
Novel Approach ◽  
N Gram ◽  
Feature Selection Techniques

In this paper, we present a novel approach for n-gram generation in text classification. The a-priori algorithm is adapted to prune word sequences by combining three feature selection techniques. Unlike the traditional two-step approach for text classification in which feature selection is performed after the n-gram construction process, our proposal performs an embedded feature elimination during the application of the a-priori algorithm. The proposed strategy reduces the number of branches to be explored, speeding up the process and making the construction of all the word sequences tractable. Our proposal has the additional advantage of constructing a low-dimensional dataset with only the features that are relevant for classification, that can be used directly without the need for a feature selection step. Experiments on text classification datasets for sentiment analysis demonstrate that our approach yields the best predictive performance when compared with other feature selection approaches, while also facilitating a better understanding of the words and phrases that explain a given task; in our case online reviews and ratings in various domains.

scholarly journals Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1564 ◽  
Author(s):  
Patrick Bradley ◽  
Sina Keller ◽  
Martin Weinmann
Keyword(s):  
Feature Selection ◽  
Dimensionality Reduction ◽  
Hyperspectral Data ◽  
Training Data ◽  
High Dimensional ◽  
Unsupervised Feature Selection ◽  
Feature Selection Techniques ◽  
The Given

In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features.

scholarly journals Comparison of feature selection techniques in classifying stroke documents

2019 ◽  
Vol 14 (3) ◽  
pp. 1244
Author(s):  
Nur Syaza Izzati Mohd Rafei ◽  
Rohayanti Hassan ◽  
RD Rohmat Saedudin ◽  
Anis Farihan Mat Raffei ◽  
Zalmiyah Zakaria ◽  
...  
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Biomedical Literature ◽  
High Dimensionality ◽  
Support Vector ◽  
Pearson’S Correlation ◽  
Pearson's Correlation ◽  
Feature Selection Techniques ◽  
Selection Phase

<span>The amount of digital biomedical literature grows that make most of the researchers facing the difficulties to manage and retrieve the required information from the Internet because this task is very challenging. The application of text classification on biomedical literature is one of the solutions in order to solve problem that have been faced by researchers but managing the high dimensionality of data being a common issue on text classification. Therefore, the aim of this research is to compare the techniques that could be used to select the relevant features for classifying biomedical text abstracts. This research focus on Pearson’s Correlation and Information Gain as feature selection techniques for reducing the high dimensionality of data. Towards this effort, we conduct and evaluate several experiments using 100 abstract of stroke documents that retrieved from PubMed database as datasets. This dataset underwent the text pre-processing that is crucial before proceed to feature selection phase. Features selection phase is involving Information Gain and Pearson Correlation technique. Support Vector Machine classifier is used in order to evaluate and compare the effectiveness of two feature selection techniques. For this dataset, Information Gain has outperformed Pearson’s Correlation by 3.3%. This research tends to extract the meaningful features from a subset of stroke documents that can be used for various application especially in diagnose the stroke disease.</span>

A Comparative Study on Feature Selection in Chinese Text Classification Problem

2013 ◽  
Vol 380-384 ◽  
pp. 2854-2857
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han
Keyword(s):  
Feature Selection ◽  
Chinese Text ◽  
Text Classification ◽  
Imbalanced Data ◽  
Classification Problem ◽  
English Text ◽  
Feature Selection Techniques ◽  
Actual Classification ◽  
Better Than

Information explosion brings lots of challenges to text classification. The dimension disaster led to a sharp increase of computational complexity and lower classification accuracy. Therefore, it is critical to use feature selection techniques before actual classification. Automatic classification of English text has been researched for many years, but little on Chinese text. In this paper, several classic feature selection methods, namely TF, IG and CHI, are compared on classifying Chinese text. Meanwhile, we take imbalanced data into consideration in the paper. Experimental results show that CHI performed better than IG and TF when the dataset is imbalanced, but no obvious difference on balanced data.

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