Machine Learning Methods with Noisy, Incomplete or Small Datasets

Cesar F Caiafa,Pere Marti-Puig,Zhe Sun,Toshihisa Tanaka,Jordi Solé-Casals

doi:10.3390/app11094132

Cesar F Caiafa, Pere Marti-Puig + Show 3 more

Open Access

https://doi.org/10.3390/app11094132

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Abstract

In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.

Highlights

In [8], Silitonga et al (Indonesia) developed prediction models to estimate the severity level of dengue based on the laboratory test results of the corresponding patients using artificial neural network (ANN) and discriminant analysis (DA) applied to very small datasets
In [1], the authors provided a unified review of decomposition methods, which includes linear decomposition, low-rank matrix/tensor factorization, sparse matrix/tensor decomposition and empirical mode decomposition (EMD) models
This paper illustrates the ability of these decomposition models to impute missing features, denoising and to artificially generate additional data samples with examples to the brain–computer interface (BCI) and epileptic EEG analysis, among others

Summary

Introduction

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. 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 as the low-quality data problem. Far from being solved, this problem still represents a fundamental and classic challenge in the artificial intelligence community The aim of this Special Issue was to collect 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.

Medical Applications

Epidemics Monitoring and Management Tools

Methodological Articles

Applications to the Industry

Other Applications

Conclusions