A hybrid feature selection method based on information theory and binary butterfly optimization algorithm
Z Sadeghian, E Akbari, H Nematzadeh - Engineering Applications of …, 2021 - Elsevier
Feature selection is the problem of finding the optimal subset of features for predicting class
labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization …
labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization …
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …
sets that can be considered correct and useful for further data mining algorithms. This paper …
[HTML][HTML] Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing
Rolling bearings are widely used in rotary machinery systems. The measured vibration
signal of any part linked to rolling bearings contains fault information when failure occurs …
signal of any part linked to rolling bearings contains fault information when failure occurs …
Unsupervised feature selection via nonnegative spectral analysis and redundancy control
In many image processing and pattern recognition problems, visual contents of images are
currently described by high-dimensional features, which are often redundant and noisy …
currently described by high-dimensional features, which are often redundant and noisy …
Feature selection with dynamic mutual information
Feature selection plays an important role in data mining and pattern recognition, especially
for large scale data. During past years, various metrics have been proposed to measure the …
for large scale data. During past years, various metrics have been proposed to measure the …
Feature selection using a neural network with group lasso regularization and controlled redundancy
We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …
of redundancy in the selected features by integrating two penalties into a single objective …
K-Means+ ID3: A novel method for supervised anomaly detection by cascading K-Means clustering and ID3 decision tree learning methods
SR Gaddam, VV Phoha… - IEEE transactions on …, 2007 - ieeexplore.ieee.org
In this paper, we present" k-means+ ID3", a method to cascade k-means clustering and the
ID3 decision tree learning methods for classifying anomalous and normal activities in a …
ID3 decision tree learning methods for classifying anomalous and normal activities in a …
RETRACTED ARTICLE: Feature selection for machine learning classification problems: a recent overview
SB Kotsiantis - Artificial intelligence review, 2014 - Springer
RETRACTED ARTICLE: Feature selection for machine learning classification problems: a
recent overview | Artificial Intelligence Review Skip to main content SpringerLink Account Menu …
recent overview | Artificial Intelligence Review Skip to main content SpringerLink Account Menu …
Measuring relevance between discrete and continuous features based on neighborhood mutual information
Measures of relevance between features play an important role in classification and
regression analysis. Mutual information has been proved an effective measure for decision …
regression analysis. Mutual information has been proved an effective measure for decision …