Machine Learning Methods with Noisy, Incomplete or Small Datasets
In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, i...
Saved in:
| Format: | Online |
|---|---|
| Sprog: | engelsk |
| Udgivet: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| Fag: | |
| Online adgang: | ONIX_20220111_9783036512884_45 |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
| _version_ | 1869530089522200576 |
|---|---|
| collection | Directory of Open Access Books |
| description | In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios. |
| format | Online |
| id | doab-20.500.12854ir-76309 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-763092024-03-30T12:51:18Z Machine Learning Methods with Noisy, Incomplete or Small Datasets Solé-Casals, Jordi Sun, Zhe Caiafa, Cesar F. Marti-Puig, Pere Tanaka, Toshihisa open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios. 2022-01-11T13:28:16Z 2022-01-11T13:28:16Z 2021 book ONIX_20220111_9783036512884_45 9783036512884 9783036512877 https://directory.doabooks.org/handle/20.500.12854/76309 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/3727 https://mdpi.com/books/pdfview/book/3727 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-1287-7 10.3390/books978-3-0365-1287-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036512884 9783036512877 316 Basel, Switzerland open access |
| spellingShingle | open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title | Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_full | Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_fullStr | Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_full_unstemmed | Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_short | Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_sort | machine learning methods with noisy incomplete or small datasets |
| topic | open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20220111_9783036512884_45 |