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[HTML][HTML] Forecasting: theory and practice
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 …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
Machine learning on big data: Opportunities and challenges
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 …
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
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 …
classification algorithms usually assume that the number of samples in each class is similar …
Big Data and cloud computing: innovation opportunities and challenges
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 …
and opportunities to improve and/or enable research and decision-support applications with …
Big data preprocessing: methods and prospects
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 …
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
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 …
generated from various sources like social media, internet of things and multimedia …
A Pearson's correlation coefficient based decision tree and its parallel implementation
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 …
established and its parallel implementation is developed in the framework of Map-Reduce …
kNN Classification: a review
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 …
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
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 …
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
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 …
composed of three methods (decision tree, gradient boosted trees and random forest) is …