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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12032 |
| _version_ | 1817998357068513280 |
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| author | Gaurav Mohindru Koushik Mondal Haider Banka |
| author_facet | Gaurav Mohindru Koushik Mondal Haider Banka |
| author_sort | Gaurav Mohindru |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T02:52:01Z |
| format | Article |
| id | doaj.art-014d1b354220429580ded2cebdc4d73b |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-14T02:52:01Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-014d1b354220429580ded2cebdc4d73b2022-12-22T02:16:15ZengWileyCAAI Transactions on Intelligence Technology2468-23222021-12-016440541610.1049/cit2.12032Different hybrid machine intelligence techniques for handling IoT‐based imbalanced dataGaurav Mohindru0Koushik Mondal1Haider Banka2Department of Computer Science and Engineering IIT(ISM)Dhanbad IndiaDepartment of Computer Centre IIT(ISM)Dhanbad IndiaDepartment of Computer Science and Engineering IIT(ISM)Dhanbad IndiaAbstract 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.https://doi.org/10.1049/cit2.12032neural netsartificial intelligencemedical computingInternet of Thingspattern classificationlearning (artificial intelligence) |
| spellingShingle | Gaurav Mohindru Koushik Mondal Haider Banka Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data CAAI Transactions on Intelligence Technology neural nets artificial intelligence medical computing Internet of Things pattern classification learning (artificial intelligence) |
| title | 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_short | Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data |
| title_sort | different hybrid machine intelligence techniques for handling iot based imbalanced data |
| topic | neural nets artificial intelligence medical computing Internet of Things pattern classification learning (artificial intelligence) |
| url | https://doi.org/10.1049/cit2.12032 |
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