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Recommendation of Job Offers Using Random Forests and Support Vector Machines [article]

Jorge Martinez
2020 Figshare  
Recommendation of Job Offers Using Random Forests and Support Vector Machines  ...  The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their useful suggestions to improve this work.  ... 
doi:10.6084/m9.figshare.11901915.v1 fatcat:lywec775svafzlkghve4sgjloq

Field selection for job categorization and recommendation to social network users

Emmanuel Malherbe, Mamadou Diaby, Mario Cataldi, Emmanuel Viennet, Marie-Aude Aufaure
2014 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)  
Nowadays, in the Web 2.0 reality, one of the most challenging task for companies that aim to manage and recommend job offers is to convey this enormous amount of information in a succinct and intelligent  ...  In this study three classes of documents are considered: job offers, job categories and social network user profiles (as potential job candidates); each class contains several fields with textual information  ...  would allow us to separate every job offer into static fields, and automatically detect fields for resumes of social network users: Headline, Experience, Formation or Interests. VI.  ... 
doi:10.1109/asonam.2014.6921646 dblp:conf/asunam/MalherbeDCVA14 fatcat:3i7bw6mjrzcojpervnuhpks43a

A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, Aimad Qazdar
2021 International Journal of Online and Biomedical Engineering (iJOE)  
Decision Tree, Support Vector Regression, and Random Forest Regression.  ...  The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission.  ...  The second part focuses on Machine Learning algorithms, that use different regression algorithms (i.e., Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression) to Build  ... 
doi:10.3991/ijoe.v17i02.20049 fatcat:gkwwdgdotrbinhjb3krjip6pya

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface

Iwona E. Weidlich, Yuri Pevzner, Benjamin T. Miller, Igor V. Filippov, H. Lee Woodcock, Bernard R. Brooks
2014 Journal of Computational Chemistry  
This new module implements some of the most recent advances in modern machine learning algorithms -Random Forest, Support Vector Machine (SVM), Stochastic Gradient Descent, Gradient Tree Boosting etc.  ...  Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a Web-based tool for SAR and QSAR modeling  ...  This research was supported in part by the Intramural Research program of the National Heart, Lung, and Blood Institute, NIH. Funding Sources  ... 
doi:10.1002/jcc.23765 pmid:25362883 pmcid:PMC4244250 fatcat:lfh4es7be5hf5byb4nqk6jg7ja

An Intelligent Model for Online Recruitment Fraud Detection

Bandar Alghamdi, Fahad Alharby
2019 Journal of Information Security  
For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed.  ...  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).  ...  Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper.  ... 
doi:10.4236/jis.2019.103009 fatcat:mpbt554535dvhifjbuhpl4oyki

Employee Attrition Estimation Using Random Forest Algorithm

Madara Pratt, Mohcine Boudhane, Sarma Cakula
2021 Baltic Journal of Modern Computing  
In this paper, the authors compare state-of-the-art solutions for the proposed machine learning algorithms using a real data set sample size of 1469.  ...  Experimental results show that the Random Forest algorithm demonstrated the best capabilities to predict the employees' attrition.  ...  Acknowledgement This research has been supported by a grant from the European Regional Development Fund project "The Study of Computer Vision Algorithms for Underwater Fish Inspection" No.  ... 
doi:10.22364/bjmc.2021.9.1.04 fatcat:6ygs7dmsanhbti2mjw2jbcfcxe

Prediction of Stock Prices using Random Forest and Support Vector Machines

2019 International journal of recent technology and engineering  
various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures.  ...  We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource  ...  Support Vector Algorithm The primary job of the supporting machine algorithm is to define an N-dimensional space that categorizes the information points differently.  ... 
doi:10.35940/ijrte.d7026.118419 fatcat:ryupz6dxnjc5njthb5u2rqdrku

From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems [article]

Steffen Schnitzer and Svenja Neitzel and Christoph Rensing
2017 arXiv   pre-print
Task selection in micro-task markets can be supported by recommender systems to help individuals to find appropriate tasks.  ...  Previous work showed that for the selection process of a micro-task the semantic aspects, such as the required action and the comprehensibility, are rated more important than factual aspects, such as the  ...  The Weka implementations of six different classifiers are used to evaluate the performances of Naive Bayes, Random Forest, K Nearest Neighbors (IBk), Support Vector Machine (SMO), a rule based classifier  ... 
arXiv:1707.06562v1 fatcat:hiil475r3zhwjbnwgccwosi6ia

Fake News Detection in Machine Learning Hybrid Model

2020 International journal of recent technology and engineering  
So we come up with a different plan to increase the accuracy by hybridizing Decision Tree and Random Forest.  ...  The spreading of fake news mainly misleads the people and some false news that led to the absence of truth and stirs up the public opinion.  ...  The extracted features are trained and predicted using three algorithms of machine learning, namely Decision Tree, Random Forest and Hybrid Model.  ... 
doi:10.35940/ijrte.a3067.059120 fatcat:qy53ikhizjggtjkyjvai5ccscu

Campus Recruitment: Academic and Employability Factors Influencing Placement Using Data Mining

Sherin K Thomas, Nimmy Francis
2021 Zenodo  
In this study, we gathered data from students that included various details about their prior and current academic records, and then used several classification algorithm using Data Mining tools (WEKA)  ...  Educational Data Mining is focused with the development of innovative methods for discovering knowledge from educational databases and applying it to educational decision-making.  ...  Figure1: Summary for test modal using Decision Tree Random Tree: The random trees classifier, unlike the Support Vector Machine (SVM), can handle a mix of categorical and numerical variable.  ... 
doi:10.5281/zenodo.5109069 fatcat:dnwns6re3bch5j3kqfmrmkcdna

Machine Learning in Official Statistics [article]

Martin Beck, Florian Dumpert, Joerg Feuerhake
2018 arXiv   pre-print
It was of particular interest to find out in which statistical areas and for which tasks machine learning is used and which methods are applied.  ...  A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and among the divisions  ...  Neural networks and support vector machines are also often used.  ... 
arXiv:1812.10422v1 fatcat:qpuvfdzbevcfxgdd7pmq2ifhmi

OER Recommendations to Support Career Development

Mohammadreza Tavakoli, Ali Faraji, Stefan T. Mol, Gabor Kismihok
2020 2020 IEEE Frontiers in Education Conference (FIE)  
As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.  ...  Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs.  ...  For this measure we used procedures from [11] 7 to define a random forest model that classifies OERs into two groups: Fit for Achieving a Skill, and Not Fit for Achieving a Skill.  ... 
doi:10.1109/fie44824.2020.9274175 fatcat:6w5tds3275fsblotkvjwmrh5bm

Application of Machine Learning in the Hotel Industry: A Critical Review

2020 Journal of Association of Arab Universities for Tourism and Hospitality  
RQ2: What are the machine learning techniques used in the hotel industry? RQ3: Which countries are using machine learning in the hotel industry?  ...  Many researchers in recent time have garnered interest in exploring and implementing the new technologies of artificial intelligence and machine learning in the hotel industry.  ...  Random Forests (RF), Gradient Boosting, FCNN, Multinomial Naive Bayes (MNB), Linear Support Vector, Extreme learning machine (ELM) support vector regression (SVR), boosted regression tree (BRT), random  ... 
doi:10.21608/jaauth.2020.38784.1060 fatcat:qqa3k4qkdra73a33o75yyqvhvm

A survey of open source tools for machine learning with big data in the Hadoop ecosystem

Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, Tawfiq Hasanin
2015 Journal of Big Data  
We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility.  ...  The available tools have advantages and drawbacks, and many have overlapping uses.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1186/s40537-015-0032-1 fatcat:zgcsiokrynfhzbmaudqf7rcll4

Utilizing GPU Performance Counters to Characterize GPU Kernels via Machine Learning [chapter]

Bob Zigon, Fengguang Song
2020 Lecture Notes in Computer Science  
Instead of relying on experts' manual analysis, this paper targets using machine learning methods to generalize GPU performance counter data to determine the characteristics of a GPU kernel as they will  ...  We choose a set of problemindependent counters as our inputs to design and compare three machine learning methods to automatically classify the execution behavior of a kernel.  ...  The Random Forest Model Design Our second machine learning model is a random forest classifier [4] , which is based upon a collection, or ensemble, of binary decision trees where the probability of each  ... 
doi:10.1007/978-3-030-50371-0_7 fatcat:5soedr5lgza5xlaprmjuhe5o3i
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