Machine Learning Methods with Noisy, Incomplete or Small Datasets
Material type:
ArticleLanguage: English Publication details: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021Description: 1 electronic resource (316 p.)ISBN: - books978-3-0365-1287-7
- 9783036512884
- 9783036512877
- Information technology industries
- 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
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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.
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