Impact of Feature Selection Technique on Email Classification

Sharaff, Aakanksha; Nagwani, Naresh Kumar; Swami, Kunal

doi:10.7763/ijke.2015.v1.10

Cited by 11 publications

(5 citation statements)

References 10 publications

(10 reference statements)

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“…So as to conquer the disadvantages of the papers alluded, and furthermore to improve the precision levels we have built up a model utilizing Natural Language Processing and Machine Learning. Using Linear SVC algorithm could give commendable level of accuracy (17). This algorithm could meet the expectations to a great extent.…”

Section: Literature Surveymentioning

confidence: 92%

Machine Learning Techniques for True and Fake Job Posting

Mehta¹,

Rajagopal²,

Gaikwad³

et al. 2022

ECS Trans.

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According to research, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer posting a job is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. The poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. Paper suggests using a machine learning model to achieve this goal. An idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life, real, and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system security is being used. Linear SVC is being used in this project for predicting real and fake job posting on a job portal.

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Section: Literature Surveymentioning

confidence: 92%

Machine Learning Techniques for True and Fake Job Posting

Mehta¹,

Rajagopal²,

Gaikwad³

et al. 2022

ECS Trans.

View full text Add to dashboard Cite

show abstract

“…There is no effect of Naïve Bayes on feature selection techniques. Further, J48 showed slight improvement with feature selection, whereas info-Gain performed better than Chi-square feature selection technique [24].…”

Section: Literature Reviewmentioning

confidence: 96%

An Intelligent Model for Online Recruitment Fraud Detection

Alghamdi¹,

Alharby²

2019

JIS

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This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

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“…In Ref. [ 25 ], the authors have used three different feature selection techniques: Chi-Square, Info Gain, and ReliefF. The authors discussed the method to identify suitable and relevant features for developing efficient machine learning based classifiers to filter spam emails.…”

Section: Related Workmentioning

confidence: 99%

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

Tripathy

Sharaff

2023

J Supercomput

Self Cite

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Sentiment analysis involves extricating and interpreting people’s views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users’ opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets.

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Impact of Feature Selection Technique on Email Classification

Cited by 11 publications

References 10 publications

Machine Learning Techniques for True and Fake Job Posting

Machine Learning Techniques for True and Fake Job Posting

An Intelligent Model for Online Recruitment Fraud Detection

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

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