Machine Learning Methods with Noisy, Incomplete or Small Datasets

Format:

Book

Author/Creator:

Solé-Casals, Jordi., Editor.

Contributor:

Sun, Zhe, Editor.

Caiafa, Cesar F., Editor.

Marti-Puig, Pere, Editor.

Tanaka, Toshihisa, Editor.

Solé-Casals, Jordi.

Sun, Zhe

Caiafa, Cesar F.

Marti-Puig, Pere

Tanaka, Toshihisa

Language:

English

Physical Description:

1 online resource (316 p.)

Place of Publication:

Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021

Language Note:

English

Summary:

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.