A review of unsupervised feature selection methods
S Solorio-Fernández, JA Carrasco-Ochoa… - Artificial Intelligence …, 2020 - Springer
In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …
many research areas; this is mainly due to their ability to identify and select relevant features …
Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review
Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …
A survey on feature selection methods
G Chandrashekar, F Sahin - Computers & electrical engineering, 2014 - Elsevier
Plenty of feature selection methods are available in literature due to the availability of data
with hundreds of variables leading to data with very high dimension. Feature selection …
with hundreds of variables leading to data with very high dimension. Feature selection …
Unsupervised feature selection via adaptive autoencoder with redundancy control
Unsupervised feature selection is one of the efficient approaches to reduce the dimension of
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …
A survey on feature selection approaches for clustering
The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …
learning tasks. One of the major challenges is the selection of relevant features from the …
Unsupervised feature selection using feature similarity
In this article, we describe an unsupervised feature selection algorithm suitable for data sets,
large in both dimension and size. The method is based on measuring similarity between …
large in both dimension and size. The method is based on measuring similarity between …
Data mining in soft computing framework: a survey
The present article provides a survey of the available literature on data mining using soft
computing. A categorization has been provided based on the different soft computing tools …
computing. A categorization has been provided based on the different soft computing tools …
Sensitivity analysis of Takagi–Sugeno fuzzy neural network
In this paper, we first define a measure of statistical sensitivity of a zero-order Takagi–
Sugeno (TS) fuzzy neural network (FNN) with respect to perturbation of weights and …
Sugeno (TS) fuzzy neural network (FNN) with respect to perturbation of weights and …
Artificial immune systems as a novel soft computing paradigm
LND Castro, JI Timmis - Soft computing, 2003 - Springer
Artificial immune systems (AIS) can be defined as computational systems inspired by
theoretical immunology, observed immune functions, principles and mechanisms in order to …
theoretical immunology, observed immune functions, principles and mechanisms in order to …
Random forest algorithm for land cover classification
AD Kulkarni, B Lowe - 2016 - scholarworks.uttyler.edu
Since the launch of the first land observation satellite Landsat-1 in 1972, many machine
learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery …
learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery …