Feature subset selection for data and feature streams: a review
Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …
hinders the modelling and descriptive analysis of the data. However, some of these data …
Feature selection techniques in the context of big data: taxonomy and analysis
HM Abdulwahab, S Ajitha, MAN Saif - Applied Intelligence, 2022 - Springer
Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …
production of big data, as enormous volumes of data with high dimensional features grow …
A latent factor analysis-based approach to online sparse streaming feature selection
Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …
decades. Current approaches commonly assume that the feature space of fixed data …
TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data
L Sun, S Si, W Ding, X Wang, J Xu - Information Fusion, 2023 - Elsevier
Obtaining informative features is crucial in imbalanced classification. However, existing
neighborhood rough set-based feature selection approaches easily overlook the diversity …
neighborhood rough set-based feature selection approaches easily overlook the diversity …
Feature selection with kernelized multi-class support vector machine
Y Guo, Z Zhang, F Tang - Pattern Recognition, 2021 - Elsevier
Feature selection is an important procedure in machine learning because it can reduce the
complexity of the final learning model and simplify the interpretation. In this paper, we …
complexity of the final learning model and simplify the interpretation. In this paper, we …
Multi-objective PSO based online feature selection for multi-label classification
Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …
data to improve the efficiency without degrading the performance of the model. In many real …
Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors
L Sun, J Zhang, W Ding, J Xu - Information Sciences, 2022 - Elsevier
Most existing imbalanced data classification models mainly focus on the classification
performance of majority class samples, and many clustering algorithms need to manually …
performance of majority class samples, and many clustering algorithms need to manually …
Active incremental feature selection using a fuzzy-rough-set-based information entropy
X Zhang, C Mei, D Chen, Y Yang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Feature selection is a popular technique of preprocessing data. In order to deal with
dynamic or large data, incremental feature selection has been developed, in which the …
dynamic or large data, incremental feature selection has been developed, in which the …
Feature interaction for streaming feature selection
Traditional feature selection methods assume that all data instances and features are known
before learning. However, it is not the case in many real-world applications that we are more …
before learning. However, it is not the case in many real-world applications that we are more …
Feature selection in threes: neighborhood relevancy, redundancy, and granularity interactivity
As a fundamental granular computing strategy, neighborhood granulation has been
acknowledged as an intuitive and effective approach to feature evaluation and selection …
acknowledged as an intuitive and effective approach to feature evaluation and selection …