• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.55-74
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• 1994

We consider an M/G/1 queueing system with multiple priority classes of jobs. Considered preemptive rules are the preemptive-resume preemptive-repeat-identical, and preemptive-repeat-different policies. These three preemptive rules will be analyzed in parallel. The key idea of analysis is based on the consideration of a busy period as composite of delay cycle. As results we present the exact Laplace-Stieltjecs(L.S) transforms of residence time and completion time in the system.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.15-23
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• 2006

Millions of documents are already on the Internet, and new documents are being formed all the time. This poses a very important problem in the management and querying of documents to classify them on the Internet by the most suitable means. However, most users have been using the document classification method based on a keyword. This method does not classify documents efficiently, and there is a weakness in the category of document that includes meaning. Document classification by a person can be very correct sometimes and often times is required. Therefore, in this paper, We wish to classify documents by using a neural network algorithm and C4.5 algorithms. We used resume data forming by XML for a document classification experiment. The result showed excellent possibilities in the document category. Therefore, We expect an applicable solution for various document classification problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.535-536
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• 2016

사람이 수많은 지원자의 이력서들을 모두 꼼꼼히 읽는 데에는 엄청난 시간과 노동이 필요하다. 만약 컴퓨터가 이력서를 알맞은 직군으로 분류해 줄 수 있다면 이러한 어려움을 해소할 수 있다. 이를 위해 본 논문에서는 알맞은 직군으로 분류하기 위한 이력서를 학습할 때에 feature를 어떤 방법으로 선택할 수 있는지 그리고 feature의 개수는 몇 개가 적절한지에 대해 알아본다.