scholarly journals A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

2016 ◽  
Vol 5 (5) ◽  
pp. 1267-1275 ◽  
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Classification Algorithms ◽  
Feature Selection Techniques

scholarly journals A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020 ◽  
pp. 11-18
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Support Vector ◽  
Classification Algorithms ◽  
End User ◽  
Preparation Methods ◽  
Survey Paper ◽  
Vector Machines ◽  
Feature Selection Techniques ◽  
Phishing Detection

: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

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.

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