Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐driven machine learning models are now offering more or less wort...

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Autores principales:	Gaurav Mohindru, Koushik Mondal, Haider Banka
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/014d1b354220429580ded2cebdc4d73b
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spelling	oai:doaj.org-article:014d1b354220429580ded2cebdc4d73b2021-11-17T03:12:43ZDifferent hybrid machine intelligence techniques for handling IoT‐based imbalanced data2468-232210.1049/cit2.12032https://doaj.org/article/014d1b354220429580ded2cebdc4d73b2021-12-01T00:00:00Zhttps://doi.org/10.1049/cit2.12032https://doaj.org/toc/2468-2322Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐driven machine learning models are now offering more or less worthy results when they are certainly balanced in the input data sets. Imbalanced data occurs when an unequal distribution of classes occurs in the input datasets. Building a predictive model on the imbalanced data set would cause a model that appears to yield high accuracy but does not generalize well to the new data in the minority class. Now the time has come to look into the datasets which are not so‐called ‘balanced’ in nature but such datasets are generally encountered frequently in a workspace. To prevent creating models with false levels of accuracy, the imbalanced data should be rearranged before creating a predictive model. Those data are, sometimes, voluminous, heterogeneous and complex in nature and generate from different autonomous sources with distributed and decentralized control. The driving force is to efficiently handle these data sets using latest tools and techniques for research and commercial insights. The present article provides different such tools and techniques, in different computing frameworks, to handle such Internet of Things and other related datasets to review common techniques for handling imbalanced data in data ecosystems and offers a comparative data modelling framework in Keras for balanced and imbalanced datasets.Gaurav MohindruKoushik MondalHaider BankaWileyarticleComputational linguistics. Natural language processingP98-98.5Computer softwareQA76.75-76.765ENCAAI Transactions on Intelligence Technology, Vol 6, Iss 4, Pp 405-416 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
spellingShingle	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 Gaurav Mohindru Koushik Mondal Haider Banka Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
description	Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐driven machine learning models are now offering more or less worthy results when they are certainly balanced in the input data sets. Imbalanced data occurs when an unequal distribution of classes occurs in the input datasets. Building a predictive model on the imbalanced data set would cause a model that appears to yield high accuracy but does not generalize well to the new data in the minority class. Now the time has come to look into the datasets which are not so‐called ‘balanced’ in nature but such datasets are generally encountered frequently in a workspace. To prevent creating models with false levels of accuracy, the imbalanced data should be rearranged before creating a predictive model. Those data are, sometimes, voluminous, heterogeneous and complex in nature and generate from different autonomous sources with distributed and decentralized control. The driving force is to efficiently handle these data sets using latest tools and techniques for research and commercial insights. The present article provides different such tools and techniques, in different computing frameworks, to handle such Internet of Things and other related datasets to review common techniques for handling imbalanced data in data ecosystems and offers a comparative data modelling framework in Keras for balanced and imbalanced datasets.
format	article
author	Gaurav Mohindru Koushik Mondal Haider Banka
author_facet	Gaurav Mohindru Koushik Mondal Haider Banka
author_sort	Gaurav Mohindru
title	Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
title_short	Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
title_full	Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
title_fullStr	Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
title_full_unstemmed	Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
title_sort	different hybrid machine intelligence techniques for handling iot‐based imbalanced data
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/014d1b354220429580ded2cebdc4d73b
work_keys_str_mv	AT gauravmohindru differenthybridmachineintelligencetechniquesforhandlingiotbasedimbalanceddata AT koushikmondal differenthybridmachineintelligencetechniquesforhandlingiotbasedimbalanceddata AT haiderbanka differenthybridmachineintelligencetechniquesforhandlingiotbasedimbalanceddata
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Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

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