Entropy in Real-World Datasets and Its Impact on Machine Learning
The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel chara...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online pristup: | ONIX_20230714_9783036578484_57 |
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| _version_ | 1863743048076230656 |
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| collection | Directory of Open Access Books |
| description | The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the reprint also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker. |
| format | Online |
| id | doab-20.500.12854ir-101358 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1013582024-03-30T12:51:28Z Entropy in Real-World Datasets and Its Impact on Machine Learning Kozak, Jan Juszczuk, Przemysław machine learning optical networks imbalanced data one-class classification entropy measure real-world data preprocessing decision table classification query set decision tree differential cryptanalysis metaheuristics symmetric block ciphers memetic algorithms DES simulated annealing COVID-19 vaccination agent-based modelling dynamic stochastic general equilibrium models scenario analyses validation of results stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model classification measure quality of classification quality measure preference-driven classification fast iterative filtering parameter adaptive refined composite multiscale fluctuation-based dispersion entropy rotating machinery fault diagnosis short-term demand prediction bike-sharing travel characteristics analysis hybrid TCN-GRU model distributed data decision tables information systems decision trees decision rules tests reducts association rules Pawlak conflict analysis model independent data sources coalitions dispersed data length support greedy heuristics feature selection rough sets action units automatic translation sign language entropy of real data thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the reprint also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker. 2023-07-14T14:26:45Z 2023-07-14T14:26:45Z 2023 book ONIX_20230714_9783036578484_57 9783036578484 9783036578491 https://directory.doabooks.org/handle/20.500.12854/101358 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7453 https://mdpi.com/books/pdfview/book/7453 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7849-1 10.3390/books978-3-0365-7849-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036578484 9783036578491 278 Basel open access |
| spellingShingle | machine learning optical networks imbalanced data one-class classification entropy measure real-world data preprocessing decision table classification query set decision tree differential cryptanalysis metaheuristics symmetric block ciphers memetic algorithms DES simulated annealing COVID-19 vaccination agent-based modelling dynamic stochastic general equilibrium models scenario analyses validation of results stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model classification measure quality of classification quality measure preference-driven classification fast iterative filtering parameter adaptive refined composite multiscale fluctuation-based dispersion entropy rotating machinery fault diagnosis short-term demand prediction bike-sharing travel characteristics analysis hybrid TCN-GRU model distributed data decision tables information systems decision trees decision rules tests reducts association rules Pawlak conflict analysis model independent data sources coalitions dispersed data length support greedy heuristics feature selection rough sets action units automatic translation sign language entropy of real data thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title | Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title_full | Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title_fullStr | Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title_full_unstemmed | Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title_short | Entropy in Real-World Datasets and Its Impact on Machine Learning |
| title_sort | entropy in real world datasets and its impact on machine learning |
| topic | machine learning optical networks imbalanced data one-class classification entropy measure real-world data preprocessing decision table classification query set decision tree differential cryptanalysis metaheuristics symmetric block ciphers memetic algorithms DES simulated annealing COVID-19 vaccination agent-based modelling dynamic stochastic general equilibrium models scenario analyses validation of results stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model classification measure quality of classification quality measure preference-driven classification fast iterative filtering parameter adaptive refined composite multiscale fluctuation-based dispersion entropy rotating machinery fault diagnosis short-term demand prediction bike-sharing travel characteristics analysis hybrid TCN-GRU model distributed data decision tables information systems decision trees decision rules tests reducts association rules Pawlak conflict analysis model independent data sources coalitions dispersed data length support greedy heuristics feature selection rough sets action units automatic translation sign language entropy of real data thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | machine learning optical networks imbalanced data one-class classification entropy measure real-world data preprocessing decision table classification query set decision tree differential cryptanalysis metaheuristics symmetric block ciphers memetic algorithms DES simulated annealing COVID-19 vaccination agent-based modelling dynamic stochastic general equilibrium models scenario analyses validation of results stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model classification measure quality of classification quality measure preference-driven classification fast iterative filtering parameter adaptive refined composite multiscale fluctuation-based dispersion entropy rotating machinery fault diagnosis short-term demand prediction bike-sharing travel characteristics analysis hybrid TCN-GRU model distributed data decision tables information systems decision trees decision rules tests reducts association rules Pawlak conflict analysis model independent data sources coalitions dispersed data length support greedy heuristics feature selection rough sets action units automatic translation sign language entropy of real data thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20230714_9783036578484_57 |