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Balance Learning To Rank In Big Data

Iftikhar Ahmad, Guanqun Cao, M. Gabbouj, Weiyi Xie, Honglei Zhang
2014 Zenodo  
Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014  ...  In this paper, we contribute to scaling up the learning to rank algorithm over Big Data.  ...  CONCLUSION In this paper, we aim to scale up the learning to rank method by balancing the distributed ranking models toward Big Data, and subsequently improve its performance.  ... 
doi:10.5281/zenodo.44026 fatcat:bxtkkfi3zrhmdacoxulv3ism5a

Experimental evaluation of ensemble classifiers for imbalance in Big Data

Mario Juez-Gil, Álvar Arnaiz-González, Juan J. Rodríguez, César García-Osorio
2021 Applied Soft Computing  
A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced.  ...  The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.  ...  Acknowledgments The project leading to these results has received funding from ''la Caixa'' Foundation, Spain, under agreement LCF/PR/PR18/51130007.  ... 
doi:10.1016/j.asoc.2021.107447 fatcat:4glhtjzn4vbbndrdm64hhwxtj4

Gram-Schmidt Orthogonalization for Feature Ranking and Selection — A Case Study of Claim Prediction

Yuni Rosita Dewi, Department of Mathematics, Universitas Indonesia, Indonesia, Hendri Murfi, Yudi Satri
2020 International Journal of Machine Learning and Computing  
One of machine learning paradigms to handle the problem of the big data is dimensionality reduction by using a feature selection method.  ...  The frequency of claim predictions is highly increasing that head the problem of big data in terms of both the number of features and the number of policyholders.  ...  Her research focused on the application of machine learning for big data.  ... 
doi:10.18178/ijmlc.2020.10.1.898 fatcat:6tjjqey5arft3elyymrxn47z7u

Semantics in the Deep: Semantic Analytics for Big Data

Dimitrios Koutsomitropoulos, Spiridon Likothanassis, Panos Kalnis
2019 Data  
The role of neural networks and deep learning as a means to strike a balance between user aspiration, sponsored search and big data generators is thoroughly reviewed in "Neural Networks in Big Data and  ...  Among others, web ranking, learning to rank and semantic recommendation algorithms and systems are presented, and additional deep learning extensions are being examined.  ... 
doi:10.3390/data4020063 fatcat:ezudf4hfqjguldo3nst5oop5vi

Editorial: Special Issue on Recent Advances in Cognitive Learning and Data Analysis

Jinchang Ren, Amir Hussain, Jiangbin Zheng, Cheng-Lin Liu, Bin Luo
2020 Cognitive Computation  
is a new trend in combining AI machine learning with multimodal big data analytics.  ...  To this end, cognitive modeling and cognitive systems have attracted increasing attention under the framework of big data enabled machine learning, especially the sparse representation and sparse learning  ...  To this end, cognitive modeling and cognitive systems have attracted increasing attention under the framework of big data enabled machine learning, especially the sparse representation and sparse learning  ... 
doi:10.1007/s12559-020-09737-1 fatcat:u3kpkx7ervekpk7slnfzqdaniy

REVIEW ON PERFORMANCE IMPROVEMENT OF HETEROGENEOUS HADOOP CLUSTER USING RANKING ALGORITHM

Shivani Soni
2017 International Journal of Advanced Research in Computer Science  
The proposed approach balance and reorganize the input data dynamically in accordance with each node capability in an heterogeneous nature.  ...  Block placement strategy, page ranking algorithm and sampling algorithm strategies are used in the proposed approach.  ...  PROBLEM DOMAIN Hadoop handles the Big data, which is now become a great deal to manage because of generation of data in every single minute. Big data is also the opportunity for business.  ... 
doi:10.26483/ijarcs.v8i9.5188 fatcat:c456gz322zd4lbpvfqkrd4m6fy

Similarity Based Prediction System using Machine Learning Algorithms in Big Data Analytics

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Big Data is a noteworthy environment to maintain the diversity of the huge amount of data.  ...  The big data utilizes machine learning algorithms to process large datasets which comes from various places such as histories, weblogs, and data repositories, large datasets and data warehousing, etc.  ...  To do the data analytical process, the big data use Machine Learning algorithms. Machine learning algorithms help to improve automatically by machines through experiences.  ... 
doi:10.35940/ijitee.l3524.1081219 fatcat:yiq3x3ohrvb45cv3x7lfz62w2u

Research on the Changing Trend of Employment-Relevant Terms Based on Internet Big Data Analysis

Yang Wei, K.H.M. Mansur, Y. Fu
2021 E3S Web of Conferences  
The research result will facilitate application of big data technology to teaching administration in colleges, and provide a guide for college students to plan their study of vocational skills.  ...  employment, analyzed the changes in the ranking of these terms and phrases, and visualized the changing trend in the attention to employment skills from 2017 to 2019.  ...  This study attempts to visualize the changes in the attention to employment skills and capacities based on big data analysis.  ... 
doi:10.1051/e3sconf/202125101050 fatcat:q2iy5kyvhbbanbmobawpmh2wqi

"Learning to rank for information retrieval from user interactions" by K. Hofmann, S. Whiteson, A. Schuth, and M. de Rijke with Martin Vesely as coordinator

Katja Hofmann, Shimon Whiteson, Anne Schuth, Maarten de Rijke
2014 ACM SIGWEB Newsletter  
In this article we give an overview of our recent work on online learning to rank for information retrieval (IR).  ...  Third, the amount of feedback and therefore the quality of learning is limited by the number of user interactions, so it is important to use the observed data as effectively as possible.  ...  ACKNOWLEDGMENTS This article summarizes research presented in Katja Hofmann's PhD dissertation.  ... 
doi:10.1145/2591453.2591458 fatcat:pcsnnsvienemjbzn2vrvjovkim

Adaptive Load Balanced Routing in IOT Networks: A Distributed Learning Approach

Rzgar Sirwan, Muzhir Ani
2019 passer  
We presented a learning solution then, for adapting devices' communication parameters to the environment to maximize the reliability and load balancing efficiency in data transmissions.  ...  In this regard, first, we assessed low-complexity distributed learning methods that can be applied to IoT communications.  ...  from the conventional data. it is now known as the "big data" problem, and the IoT data have quickly been turned into the IoT big data.  ... 
doi:10.24271/psr.19 fatcat:hfkic6a43vbl7esayakzko7g5a

Customer churn prediction in telecom using machine learning in big data platform

Abdelrahim Kasem Ahmad, Assef Jafar, Kadan Aljoumaa
2019 Journal of Big Data  
The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection.  ...  The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company.  ...  Ethics approval and consent to participate All authors give ethics approval and consent to participate in submission and review process. Funding The authors declare that they have no funding.  ... 
doi:10.1186/s40537-019-0191-6 fatcat:yocjijb24rgfddwwgcs2hf4is4

ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem

Isaac Triguero, Sara del Río, Victoria López, Jaume Bacardit, José M. Benítez, Francisco Herrera
2015 Knowledge-Based Systems  
The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics.  ...  In this work we describe the methodology that won the ECBDL'14 big data challenge for a bioinformatics big data problem.  ...  ROSEFW-RF: The winner algorithm for the ECBDL '14 Big Data Competition: An extremely imbalanced big data bioinformatics problem Introduction Data mining and machine learning techniques [1] have become  ... 
doi:10.1016/j.knosys.2015.05.027 fatcat:iwuatu7hrbcrbcfgaczzz52fiy

Perceptions and Expectancies of Malaysian Students on Cultural Elements in Foreign Textbooks

Precintha Rubini James, Azlina Abdul Aziz
2020 International Journal of Academic Research in Business and Social Sciences  
In conclusion, there are various aspects that need improvements in constructing textbooks, to produce good language users.  ...  Many argue on the cultural gap between the ESL learners in Malaysia and the usage of foreign textbooks in the teaching and learning of English Language.  ...  support in guiding me to complete the article.  ... 
doi:10.6007/ijarbss/v10-i4/7122 fatcat:yucn7sfe2vd2td3xqskskza4vu

Machine learning for big visual analysis

Jun Yu, Xue Mei, Fatih Porikli, Jason Corso
2018 Machine Vision and Applications  
Acknowledgements The work was supported in part by the NSFC-61622205 and in part the NSFC-61472110.  ...  This strategy makes the rank-sparsity balance more tunable.  ...  The article entitled "Rank-Sparsity Balanced Representation for Subspace Clustering" proposed a new model that can balance the rank and sparsity well.  ... 
doi:10.1007/s00138-018-0948-5 fatcat:puwirktcpjg5bdfc4wxvuw77ua

Beta-Boosted Ensemble for Big Credit Scoring Data [chapter]

Maciej Zieba, Wolfgang Karl Härdle
2018 Handbook of Big Data Analytics  
The main idea of the approach is to incorporate separate beta binomial distributions for each of the classes to generate balanced datasets that are further used to construct base learners that constitute  ...  The proposed solutions are tested on two big datasets on credit scoring.  ...  Further, we assign individual ranking position for each of the considered Beta-boosted ensemble for big credit data points with individual rankings for each class.  ... 
doi:10.1007/978-3-319-18284-1_21 fatcat:p4io2yuymnekvc547wtzdxljs4
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