[HTML][HTML] Stability of feature selection algorithm: A review
Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …
of the problem through the analysis of the most relevant features. Feature selection aims at …
Recent advances and emerging challenges of feature selection in the context of big data
In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in hel** reduce high-dimensionality in machine learning …
selection has a key role to play in hel** reduce high-dimensionality in machine learning …
Simultaneous feature selection and discretization based on mutual information
Recently mutual information based feature selection criteria have gained popularity for their
superior performances in different applications of pattern recognition and machine learning …
superior performances in different applications of pattern recognition and machine learning …
EEG-based automatic emotion recognition: Feature extraction, selection and classification methods
P Ackermann, C Kohlschein, JA Bitsch… - 2016 IEEE 18th …, 2016 - ieeexplore.ieee.org
Automatic emotion recognition is an interdisciplinary research field which deals with the
algorithmic detection of human affect, eg anger or sadness, from a variety of sources, such …
algorithmic detection of human affect, eg anger or sadness, from a variety of sources, such …
Stable bagging feature selection on medical data
S Alelyani - Journal of Big Data, 2021 - Springer
In the medical field, distinguishing genes that are relevant to a specific disease, let's say
colon cancer, is crucial to finding a cure and understanding its causes and subsequent …
colon cancer, is crucial to finding a cure and understanding its causes and subsequent …
Robust feature selection for microarray data based on multicriterion fusion
Feature selection often aims to select a compact feature subset to build a pattern classifier
with reduced complexity, so as to achieve improved classification performance. From the …
with reduced complexity, so as to achieve improved classification performance. From the …
Feature selection stability and accuracy of prediction models for genomic prediction of residual feed intake in pigs using machine learning
Feature selection (FS, ie, selection of a subset of predictor variables) is essential in high-
dimensional datasets to prevent overfitting of prediction/classification models and reduce …
dimensional datasets to prevent overfitting of prediction/classification models and reduce …
Sparse Hilbert Schmidt independence criterion and surrogate-kernel-based feature selection for hyperspectral image classification
Designing an effective criterion to select a subset of features is a challenging problem for
hyperspectral image classification. In this paper, we develop a feature selection method to …
hyperspectral image classification. In this paper, we develop a feature selection method to …
Radiomics for discriminating benign and malignant salivary gland tumors; which radiomic feature categories and MRI sequences should be used?
Simple Summary MRI radiomics shows promise in discriminating salivary gland tumors
(SGTs) but a consistent radiomics signature has not emerged, partly due to the multitude of …
(SGTs) but a consistent radiomics signature has not emerged, partly due to the multitude of …
Multilayer perceptron neural network technique for fraud detection
AM Mubarek, E Adalı - 2017 International Conference on …, 2017 - ieeexplore.ieee.org
Fraud detection is an enduring topic that pose a threat to banking, insurance, financial
sectors and information security systems such as intrusion detection systems (IDS), etc. Data …
sectors and information security systems such as intrusion detection systems (IDS), etc. Data …