Ensembles for feature selection: A review and future trends
V Bolón-Canedo, A Alonso-Betanzos - Information fusion, 2019 - Elsevier
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 …
A survey of alzheimer's disease early diagnosis methods for cognitive assessment
Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A
high rate of deaths and high cost for detection, treatments, and patients care count amongst …
high rate of deaths and high cost for detection, treatments, and patients care count amongst …
An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance
J Gasienica-Jozkowy, M Knapik… - Integrated Computer …, 2021 - content.iospress.com
Today's deep learning architectures, if trained with proper dataset, can be used for object
detection in marine search and rescue operations. In this paper a dataset for maritime …
detection in marine search and rescue operations. In this paper a dataset for maritime …
On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles
HO Marques, L Swersky, J Sander… - Data Mining and …, 2023 - Springer
It has been shown that unsupervised outlier detection methods can be adapted to the one-
class classification problem (Janssens and Postma, in: Proceedings of the 18th annual …
class classification problem (Janssens and Postma, in: Proceedings of the 18th annual …
Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics
One of the most challenging issues when facing a classification problem is to deal with
imbalanced datasets. Recently, ensemble classification techniques have proven to be very …
imbalanced datasets. Recently, ensemble classification techniques have proven to be very …
Developing a generic framework for anomaly detection
The fusion of one-class classifiers (OCCs) has been shown to exhibit promising
performance in a variety of machine learning applications. The ability to assess the similarity …
performance in a variety of machine learning applications. The ability to assess the similarity …
Dynamic ensembles of exemplar-SVMs for still-to-video face recognition
Face recognition (FR) plays an important role in video surveillance by allowing to accurately
recognize individuals of interest over a distributed network of cameras. Systems for still-to …
recognize individuals of interest over a distributed network of cameras. Systems for still-to …
Modified Mahalanobis Taguchi system for imbalance data classification
M El-Banna - Computational Intelligence and Neuroscience, 2017 - Wiley Online Library
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary
classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for …
classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for …
Fuzzy clustering of maize plant-height patterns using time series of UAV remote-sensing images and variety traits
The application of high-throughput phenotyping (HTP) techniques based on unmanned
aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding …
aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding …
Novel clustering-based pruning algorithms
P Zyblewski, M Woźniak - Pattern Analysis and Applications, 2020 - Springer
One of the crucial problems of designing a classifier ensemble is the proper choice of the
base classifier line-up. Basically, such an ensemble is formed on the basis of individual …
base classifier line-up. Basically, such an ensemble is formed on the basis of individual …