[HTML][HTML] Forecasting: theory and practice

F Petropoulos, D Apiletti, V Assimakopoulos… - International Journal of …, 2022‏ - Elsevier
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Machine learning on big data: Opportunities and challenges

L Zhou, S Pan, J Wang, AV Vasilakos - Neurocomputing, 2017‏ - Elsevier
Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

[HTML][HTML] A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data

Z Xu, D Shen, T Nie, Y Kou - Journal of Biomedical Informatics, 2020‏ - Elsevier
The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

Big Data and cloud computing: innovation opportunities and challenges

C Yang, Q Huang, Z Li, K Liu, F Hu - International Journal of Digital …, 2017‏ - Taylor & Francis
Big Data has emerged in the past few years as a new paradigm providing abundant data
and opportunities to improve and/or enable research and decision-support applications with …

Big data preprocessing: methods and prospects

S García, S Ramírez-Gallego, J Luengo, JM Benítez… - Big data analytics, 2016‏ - Springer
The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

The state of the art and taxonomy of big data analytics: view from new big data framework

A Mohamed, MK Najafabadi, YB Wah… - Artificial intelligence …, 2020‏ - Springer
Big data has become a significant research area due to the birth of enormous data
generated from various sources like social media, internet of things and multimedia …

A Pearson's correlation coefficient based decision tree and its parallel implementation

Y Mu, X Liu, L Wang - Information Sciences, 2018‏ - Elsevier
In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is
established and its parallel implementation is developed in the framework of Map-Reduce …

kNN Classification: a review

PK Syriopoulos, NG Kalampalikis, SB Kotsiantis… - Annals of Mathematics …, 2023‏ - Springer
The k-nearest neighbors (k/NN) algorithm is a simple yet powerful non-parametric classifier
that is robust to noisy data and easy to implement. However, with the growing literature on …

Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data

I Triguero, D García‐Gil, J Maillo… - … : Data Mining and …, 2019‏ - Wiley Online Library
The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …

Multi-step forecasting for big data time series based on ensemble learning

A Galicia, R Talavera-Llames, A Troncoso… - Knowledge-Based …, 2019‏ - Elsevier
This paper presents ensemble models for forecasting big data time series. An ensemble
composed of three methods (decision tree, gradient boosted trees and random forest) is …