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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Evolving classifier TEDAClass for big data
AU - Kangin, Dmitry
AU - Angelov, Plamen Parvanov
AU - Iglesias, Jose Antonio
AU - Sanchis, Araceli
PY - 2015/8
Y1 - 2015/8
N2 - In the era of big data, huge amounts of data are generated and updated every day, and their processing and analysis is an important challenge today. In order to tackle this challenge, it is necessary to develop specific techniques which can process large volume of data within limited run times.TEDA is a new systematic framework for data analytics, which is based on the typicality and eccentricity of the data. This framework is spatially-aware, non-frequentist and non-parametric. TEDA can be used for development of alternative machine learning methods, in this work, we will use it for classification (TEDAClass). Specifically, we present a TEDAClass based approach which can process huge amounts of data items using a novel parallelization technique. Using this parallelization, we make possible the scalability of TEDAClass. In that way, the proposed approach is particularly useful for various applications, as it opens the doors for high-performance big data processing, which could be particularly useful for healthcare, banking, scientific and many other purposes.
AB - In the era of big data, huge amounts of data are generated and updated every day, and their processing and analysis is an important challenge today. In order to tackle this challenge, it is necessary to develop specific techniques which can process large volume of data within limited run times.TEDA is a new systematic framework for data analytics, which is based on the typicality and eccentricity of the data. This framework is spatially-aware, non-frequentist and non-parametric. TEDA can be used for development of alternative machine learning methods, in this work, we will use it for classification (TEDAClass). Specifically, we present a TEDAClass based approach which can process huge amounts of data items using a novel parallelization technique. Using this parallelization, we make possible the scalability of TEDAClass. In that way, the proposed approach is particularly useful for various applications, as it opens the doors for high-performance big data processing, which could be particularly useful for healthcare, banking, scientific and many other purposes.
KW - classifier
KW - typicality
KW - eccentricity
KW - Big Data
KW - TEDA
KW - AnYa
KW - Evolving Systems for Big Data Analytics
U2 - 10.1016/j.procs.2015.07.274
DO - 10.1016/j.procs.2015.07.274
M3 - Journal article
VL - 53
SP - 9
EP - 18
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -