scholarly journals Improved ICHI square feature selection method for Arabic classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
Hadeel N. Alshaer ◽  
Mohammed A. Otair ◽  
Laith Abualigah
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Selection Methods ◽  
Chi Square ◽  
Feature Selection Problem ◽  
Time To Build ◽  
Arabic Text Classification ◽  
Almost All ◽  
Text And Data Mining

<span>Feature selection problem is one of the main important problems in the text and data mining domain. </span><span>This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.</span>

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

Impact of feature selection on classification via clustering techniques in software defect prediction

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
F.E. Usman-Hamza ◽  
A.F. Atte ◽  
A.O. Balogun ◽  
H.A. Mojeed ◽  
A.O. Bajeh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Predictive Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Selection Methods ◽  
Clustering Techniques ◽  
Software Defect ◽  
The Impact

Software testing using software defect prediction aims to detect as many defects as possible in software before the software release. This plays an important role in ensuring quality and reliability. Software defect prediction can be modeled as a classification problem that classifies software modules into two classes: defective and non-defective; and classification algorithms are used for this process. This study investigated the impact of feature selection methods on classification via clustering techniques for software defect prediction. Three clustering techniques were selected; Farthest First Clusterer, K-Means and Make-Density Clusterer, and three feature selection methods: Chi-Square, Clustering Variation, and Information Gain were used on software defect datasets from NASA repository. The best software defect prediction model was farthest-first using information gain feature selection method with an accuracy of 78.69%, precision value of 0.804 and recall value of 0.788. The experimental results showed that the use of clustering techniques as a classifier gave a good predictive performance and feature selection methods further enhanced their performance. This indicates that classification via clustering techniques can give competitive results against standard classification methods with the advantage of not having to train any model using labeled dataset; as it can be used on the unlabeled datasets.Keywords: Classification, Clustering, Feature Selection, Software Defect PredictionVol. 26, No 1, June, 2019

A NEW FEATURE SELECTION METHOD FOR TEXT CLASSIFICATION

2007 ◽  
Vol 21 (02) ◽  
pp. 423-438 ◽  
Author(s):  
GULDEN UCHYIGIT ◽  
KEITH CLARK
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Feature Selection Method ◽  
Feature Space ◽  
Selection Method ◽  
Computational Time ◽  
Small Subset ◽  
Selection Methods ◽  
New Feature

Text classification is the problem of classifying a set of documents into a pre-defined set of classes. A major problem with text classification problems is the high dimensionality of the feature space. Only a small subset of these words are feature words which can be used in determining a document's class, while the rest adds noise and can make the results unreliable and significantly increase computational time. A common approach in dealing with this problem is feature selection where the number of words in the feature space are significantly reduced. In this paper we present the experiments of a comparative study of feature selection methods used for text classification. Ten feature selection methods were evaluated in this study including the new feature selection method, called the GU metric. The other feature selection methods evaluated in this study are: Chi-Squared (χ2) statistic, NGL coefficient, GSS coefficient, Mutual Information, Information Gain, Odds Ratio, Term Frequency, Fisher Criterion, BSS/WSS coefficient. The experimental evaluations show that the GU metric obtained the best F1 and F2 scores. The experiments were performed on the 20 Newsgroups data sets with the Naive Bayesian Probabilistic Classifier.

scholarly journals Opinion classification on a social network by a novel feature selection technique

2020 ◽  
Vol 20 (2) ◽  
pp. 960
Author(s):  
Atchara Choompol ◽  
Panida Songram ◽  
Phattahanaphong Chomphuwiset
Keyword(s):  
Feature Selection ◽  
Social Network ◽  
Association Rules ◽  
Information Gain ◽  
Feature Selection Method ◽  
Tuning Parameter ◽  
Computational Time ◽  
Chi Square ◽  
Feature Selection Technique ◽  
Filter Model

Most of the opinion comments on social networks are short and ambiguous. In general, opinion classification on the comments is difficult because of lacking dominant features. A feature extraction technique is therefore necessary for improving accuracy of the classification and computational time. This paper proposes an effective feature selection method for opinion classification on a social network. The proposed method selects features based on the concept of a filter model, together with association rules. Support and confidence are used to calculate the weights of features. The features with high weight are selected for classification. Unlike supports in association rules, supports in our method are normalized to 0-1 to remove outlier supports. Moreover, a tuning parameter is used to emphasize the degree of support or confidence. The experimental results show that the proposed method provides high classification efficiency. The proposed method outperforms Information Gain, Chi-Square, and Gini Index in both computational time and accuracy.

scholarly journals Improved Feature-Selection Method Considering the Imbalance Problem in Text Categorization

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Jieming Yang ◽  
Zhaoyang Qu ◽  
Zhiying Liu
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Information Gain ◽  
Feature Selection Method ◽  
Support Vector ◽  
Selection Methods ◽  
Document Collections ◽  
Imbalance Problem ◽  
Important Approach ◽  
Selection Algorithms

The filtering feature-selection algorithm is a kind of important approach to dimensionality reduction in the field of the text categorization. Most of filtering feature-selection algorithms evaluate the significance of a feature for category based on balanced dataset and do not consider the imbalance factor of dataset. In this paper, a new scheme was proposed, which can weaken the adverse effect caused by the imbalance factor in the corpus. We evaluated the improved versions of nine well-known feature-selection methods (Information Gain, Chi statistic, Document Frequency, Orthogonal Centroid Feature Selection, DIA association factor, Comprehensive Measurement Feature Selection, Deviation from Poisson Feature Selection, improved Gini index, and Mutual Information) using naïve Bayes and support vector machines on three benchmark document collections (20-Newsgroups, Reuters-21578, and WebKB). The experimental results show that the improved scheme can significantly enhance the performance of the feature-selection methods.

scholarly journals Enhanced Classification Method for Phishing Emails Detection

2020 ◽  
Vol 3 (1) ◽  
pp. 58-63
Author(s):  
Y. Mansour Mansour ◽  
Majed A. Alenizi
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Search Space ◽  
Selection Method ◽  
Classification Model ◽  
Selection Methods ◽  
Accuracy Rate ◽  
Communication Method

Emails are currently the main communication method worldwide as it proven in its efficiency. Phishing emails in the other hand is one of the major threats which results in significant losses, estimated at billions of dollars. Phishing emails is a more dynamic problem, a struggle between the phishers and defenders where the phishers have more flexibility in manipulating the emails features and evading the anti-phishing techniques. Many solutions have been proposed to mitigate the phishing emails impact on the targeted sectors, but none have achieved 100% detection and accuracy. As phishing techniques are evolving, the solutions need to be evolved and generalized in order to mitigate as much as possible. This article presents a new emergent classification model based on hybrid feature selection method that combines two common feature selection methods, Information Gain and Genetic Algorithm that keep only significant and high-quality features in the final classifier. The Proposed hybrid approach achieved 98.9% accuracy rate against phishing emails dataset comprising 8266 instances and results depict enhancement by almost 4%. Furthermore, the presented technique has contributed to reducing the search space by reducing the number of selected features.

scholarly journals The Impact of Feature Selection Methods for Classifying Arabic Textual Data

2019 ◽  
Vol 8 (4) ◽  
pp. 1333-1338
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Feature Space ◽  
Support Vector ◽  
Selection Methods ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Selection Algorithms ◽  
The Impact

Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x2 ), Information Gain (IG), and Correlation (CFS). These algorithms have been investigated widely for English text, while studies for Arabic text are still limited. In this paper, we investigated four well-known algorithms: Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree against benchmark Arabic textual datasets, called Saudi Press Agency (SPA) to evaluate the impact of feature selection methods. Using the WEKA tool, we have experimented the application of the four mentioned classification algorithms with and without feature selection algorithms. The results provided clear evidence that the three feature selection methods often improves classification accuracy by eliminating irrelevant features.

scholarly journals A Comparative Analysis of Filter-Based Feature Selection Methods for Software Fault Prediction

2021 ◽  
pp. 1-7
Author(s):  
Thị Minh Phương Hà ◽  
Thi My Hanh Le ◽  
Thanh Binh Nguyen
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Information Gain ◽  
Feature Selection Method ◽  
Computation Time ◽  
Fault Prediction ◽  
Software Systems ◽  
Selection Methods ◽  
Software Fault Prediction

The rapid growth of data has become a huge challenge for software systems. The quality of fault predictionmodel depends on the quality of software dataset. High-dimensional data is the major problem that affects the performance of the fault prediction models. In order to deal with dimensionality problem, feature selection is proposed by various researchers. Feature selection method provides an effective solution by eliminating irrelevant and redundant features, reducing computation time and improving the accuracy of the machine learning model. In this study, we focus on research and synthesis of the Filter-based feature selection with several search methods and algorithms. In addition, five filter-based feature selection methods are analyzed using five different classifiers over datasets obtained from National Aeronautics and Space Administration (NASA) repository. The experimental results show that Chi-Square and Information Gain methods had the best influence on the results of predictive models over other filter ranking methods.

An Extensive Text Mining Study for the Turkish Language

2021 ◽  
pp. 272-306
Author(s):  
Durmuş Özkan Şahin ◽  
Erdal Kılıç
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Language Processing ◽  
Information Gain ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Chi Square

In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.

scholarly journals Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4182-4189
Author(s):  
Qiang Huang ◽  
Tingyu Xia ◽  
Huiyan Sun ◽  
Makoto Yamada ◽  
Yi Chang
Keyword(s):  
Feature Selection ◽  
Rapid Development ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Selection Methods ◽  
Feature Selection Problem ◽  
Unsupervised Feature Selection ◽  
Signed Networks ◽  
Signed Network ◽  
Nonlinear Feature

With the rapid development of social media services in recent years, relational data are explosively growing. The signed network, which consists of a mixture of positive and negative links, is an effective way to represent the friendly and hostile relations among nodes, which can represent users or items. Because the features associated with a node of a signed network are usually incomplete, noisy, unlabeled, and high-dimensional, feature selection is an important procedure to eliminate irrelevant features. However, existing network-based feature selection methods are linear methods, which means they can only select features that having the linear dependency on the output values. Moreover, in many social data, most nodes are unlabeled; therefore, selecting features in an unsupervised manner is generally preferred. To this end, in this paper, we propose a nonlinear unsupervised feature selection method for signed networks, called SignedLasso. This method can select a small number of important features with nonlinear associations between inputs and output from a high-dimensional data. More specifically, we formulate unsupervised feature selection as a nonlinear feature selection problem with the Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso), which can find a small number of features in a nonlinear manner. Then, we propose the use of a deep learning-based node embedding to represent node similarity without label information and incorporate the node embedding into the HSIC Lasso. Through experiments on two real world datasets, we show that the proposed algorithm is superior to existing linear unsupervised feature selection methods.

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