[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
A review of feature selection methods based on mutual information
JR Vergara, PA Estévez - Neural computing and applications, 2014 - Springer
In this work, we present a review of the state of the art of information-theoretic feature
selection methods. The concepts of feature relevance, redundance, and complementarity …
selection methods. The concepts of feature relevance, redundance, and complementarity …
Building an intrusion detection system using a filter-based feature selection algorithm
Redundant and irrelevant features in data have caused a long-term problem in network
traffic classification. These features not only slow down the process of classification but also …
traffic classification. These features not only slow down the process of classification but also …
Classification of perceived mental stress using a commercially available EEG headband
Human stress is a serious health concern, which must be addressed with appropriate
actions for a healthy society. This paper presents an experimental study to ascertain the …
actions for a healthy society. This paper presents an experimental study to ascertain the …
Normalized mutual information feature selection
PA Estévez, M Tesmer, CA Perez… - IEEE Transactions on …, 2009 - ieeexplore.ieee.org
A filter method of feature selection based on mutual information, called normalized mutual
information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti's …
information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti's …
Data preprocessing in predictive data mining
SAN Alexandropoulos, SB Kotsiantis… - The Knowledge …, 2019 - cambridge.org
A large variety of issues influence the success of data mining on a given problem. Two
primary and important issues are the representation and the quality of the dataset …
primary and important issues are the representation and the quality of the dataset …
[图书][B] Multi-sensor data fusion: an introduction
HB Mitchell - 2007 - books.google.com
The purpose of this book is to provide an introduction to the theories and techniques of multi-
sensor data fusion. The book has been designed as a text for a one-semester graduate …
sensor data fusion. The book has been designed as a text for a one-semester graduate …
A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management
Smart grid is a platform that enables the participants of electricity market to adjust their
bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of …
bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of …
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification
This paper presents a hybrid filter–wrapper feature subset selection algorithm based on
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …
A new hybrid ant colony optimization algorithm for feature selection
In this paper, we propose a new hybrid ant colony optimization (ACO) algorithm for feature
selection (FS), called ACOFS, using a neural network. A key aspect of this algorithm is the …
selection (FS), called ACOFS, using a neural network. A key aspect of this algorithm is the …