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 Hybrid Feature Selection for Amharic News Document Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Demeke Endalie ◽  
Getamesay Haile
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Selection Method ◽  
Computational Time ◽  
Features Selection ◽  
Chi Square ◽  
Document Frequency ◽  
Automatic Text Classification ◽  
The Right

Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’ selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other methods on both datasets 1and 2. The IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to 11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.

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.

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 Feature Entropy Estimation (FEE) for Malicious IoT Traffic and Detection Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tarun Dhar Diwan ◽  
Siddartha Choubey ◽  
H. S. Hota ◽  
S. B Goyal ◽  
Sajjad Shaukat Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Low Cost ◽  
Pearson Correlation ◽  
Low Complexity ◽  
Computational Time ◽  
Chi Square ◽  
Feature Selection Technique ◽  
Detection Techniques ◽  
Entropy Estimation

Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is required to design a framework for the identification of attacks more accurately, quickly, and with less complexity. Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. But this ML algorithm suffers many misclassification problems due to inappropriate and irrelevant feature size. In this paper, an in-depth study is presented to address such issues. We have presented lightweight low-cost feature selection IoT intrusion detection techniques with low complexity and high accuracy due to their low computational time. A novel feature selection technique was proposed with the integration of rank-based chi-square, Pearson correlation, and score correlation to extract relevant features out of all available features from the dataset. Then, feature entropy estimation was applied to validate the relationship among all extracted features to identify malicious traffic in IoT networks. Finally, an extreme gradient ensemble boosting approach was used to classify the features in relevant attack types. The simulation is performed on three datasets, i.e., NSL-KDD, USNW-NB15, and CCIDS2017, and results are presented on different test sets. It was observed that on the NSL-KDD dataset, accuracy was approx. 97.48%. Similarly, the accuracy of USNW-NB15 and CCIDS2017 was approx. 99.96% and 99.93%, respectively. Along with that, state-of-the-art comparison is also presented with existing techniques.

scholarly journals Sentiment Classification using Neural Network and Ensemble Model based on Genetic Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 1885-1891
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Web Sites ◽  
Information Gain ◽  
Back Propagation ◽  
Feature Selection Method ◽  
Propagation Model ◽  
Feature Selection Technique ◽  
Fast Development

The fast development of web sites and the number of product on these websites are available. The purpose of classification of sentiment is to efficiently identify opinion expressed in text. This paper compares three different optimized models including genetic optimized feature selection method, Genetic Algorithm (GA), ensemble approach that uses information gain and genetic algorithm as feature selection methods incorporated SVM model, Genetic Bagging (GB) and the next method uses optimized feature selection as feature selection technique incorporated back propagation model, Genetic Neural Network (GNN) models are compared. We are tested in sentiment analysis using sample multi-domain review datasets and movie review dataset.. These approaches are tested using various quality metrics and the results show that the Genetic Bagging (GB) technique outperforms in classifying the sentiment of the multi domain reviews and movie reviews. An empirical analysis is performed to compare the level of importance of the classifiers GB, GNN methods with McNemar’s statistical method.

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 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>

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

2018 ◽  
Vol 7 (1) ◽  
pp. 57-72
Author(s):  
H.P. Vinutha ◽  
Poornima Basavaraju
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Information Gain ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Chi Square ◽  
Traffic Pattern ◽  
Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

A CATEGORY CLASSIFICATION ALGORITHM FOR INDONESIAN AND MALAY NEWS DOCUMENTS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Jafreezal Jaafar ◽  
Zul Indra ◽  
Nurshuhaini Zamin
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Online News ◽  
Language Identification ◽  
Computational Time ◽  
Accuracy Rate ◽  
Similar Morphology ◽  
Manual Classification

Text classification (TC) provides a better way to organize information since it allows better understanding and interpretation of the content. It deals with the assignment of labels into a group of similar textual document. However, TC research for Asian language documents is relatively limited compared to English documents and even lesser particularly for news articles. Apart from that, TC research to classify textual documents in similar morphology such Indonesian and Malay is still scarce. Hence, the aim of this study is to develop an integrated generic TC algorithm which is able to identify the language and then classify the category for identified news documents. Furthermore, top-n feature selection method is utilized to improve TC performance and to overcome the online news corpora classification challenges: rapid data growth of online news documents, and the high computational time. Experiments were conducted using 280 Indonesian and 280 Malay online news documents from the year 2014 – 2015. The classification method is proven to produce a good result with accuracy rate of up to 95.63% for language identification, and 97.5%% for category classification. While the category classifier works optimally on n = 60%, with an average of 35 seconds computational time. This highlights that the integrated generic TC has advantage over manual classification, and is suitable for Indonesian and Malay news classification.

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