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[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 …
Ensembles for feature selection: A review and future trends
Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …
that combining the output of multiple models is better than using a single model, and it …
Analysis and comparison of feature selection methods towards performance and stability
The amount of gathered data is increasing at unprecedented rates for machine learning
applications such as natural language processing, computer vision, and bioinformatics. This …
applications such as natural language processing, computer vision, and bioinformatics. This …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
Random forest in remote sensing: A review of applications and future directions
A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …
trees, using a randomly selected subset of training samples and variables. This classifier …
AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets
The data-driven modern era has enabled the collection of large amounts of biomedical and
clinical data. DNA microarray gene expression datasets have mainly gained significant …
clinical data. DNA microarray gene expression datasets have mainly gained significant …
Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection
Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …
many data mining tasks, especially for processing high-dimensional data such as gene …
[HTML][HTML] Feature selection using joint mutual information maximisation
Feature selection is used in many application areas relevant to expert and intelligent
systems, such as data mining and machine learning, image processing, anomaly detection …
systems, such as data mining and machine learning, image processing, anomaly detection …
Is feature selection secure against training data poisoning?
Learning in adversarial settings is becoming an important task for application domains
where attackers may inject malicious data into the training set to subvert normal operation of …
where attackers may inject malicious data into the training set to subvert normal operation of …
A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment
Credit risk assessment has been a crucial issue as it forecasts whether an individual will
default on loan or not. Classifying an applicant as good or bad debtor helps lender to make …
default on loan or not. Classifying an applicant as good or bad debtor helps lender to make …