Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Time series forecasting of petroleum production using deep LSTM recurrent networks
Time series forecasting (TSF) is the task of predicting future values of a given sequence
using historical data. Recently, this task has attracted the attention of researchers in the area …
using historical data. Recently, this task has attracted the attention of researchers in the area …
A survey on transfer learning
A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …
training and future data must be in the same feature space and have the same distribution …
A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …
challenging problem in data mining community. This issue occurs when the number of …
Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …
A review of unsupervised feature learning and deep learning for time-series modeling
This paper gives a review of the recent developments in deep learning and unsupervised
feature learning for time-series problems. While these techniques have shown promise for …
feature learning for time-series problems. While these techniques have shown promise for …
[HTML][HTML] Assessing behavioral data science privacy issues in government artificial intelligence deployment
In today's global culture where the Internet has established itself as the main tool for
communication and commerce, the capability to massively analyze and predict citizens' …
communication and commerce, the capability to massively analyze and predict citizens' …
A review on time series data mining
T Fu - Engineering Applications of Artificial Intelligence, 2011 - Elsevier
Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …
scientific and financial applications. A time series is a collection of observations made …