[图书][B] Multisensor data fusion and machine learning for environmental remote sensing
In the last few years the scientific community has realized that obtaining a better
understanding of interactions between natural systems and the man-made environment …
understanding of interactions between natural systems and the man-made environment …
[HTML][HTML] A soft-voting ensemble based co-training scheme using static selection for binary classification problems
In recent years, a forward-looking subfield of machine learning has emerged with important
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …
Relationships between diversity of classification ensembles and single-class performance measures
S Wang, X Yao - IEEE Transactions on Knowledge and Data …, 2011 - ieeexplore.ieee.org
In class imbalance learning problems, how to better recognize examples from the minority
class is the key focus, since it is usually more important and expensive than the majority …
class is the key focus, since it is usually more important and expensive than the majority …
Comparing ensemble strategies for deep learning: An application to facial expression recognition
Recent works have shown that Convolutional Neural Networks (CNNs), because of their
effectiveness in feature extraction and classification tasks, are suitable tools to address the …
effectiveness in feature extraction and classification tasks, are suitable tools to address the …
Ten measures of diversity in classifier ensembles: limits for two classifiers
LI Kuncheva, CJ Whitaker - A DERA/IEE Workshop on …, 2001 - ieeexplore.ieee.org
Independence and dependence of classifier outputs have been debated in the recent
literature giving rise to notions such as diversity, complementarity, orthogonality, etc. There …
literature giving rise to notions such as diversity, complementarity, orthogonality, etc. There …
Knowledge discovery from remote sensing images: A review
L Wang, J Yan, L Mu, L Huang - Wiley Interdisciplinary Reviews …, 2020 - Wiley Online Library
The development of Earth observation (EO) technology has made the volume of remote
sensing data archiving continually larger, but the knowledge hidden in massive remote …
sensing data archiving continually larger, but the knowledge hidden in massive remote …
That elusive diversity in classifier ensembles
LI Kuncheva - Pattern Recognition and Image Analysis: First Iberian …, 2003 - Springer
Is “useful diversity” a myth? Many experiments and the little available theory on diversity in
classifier ensembles are either inconclusive, too heavily assumption-bound or openly non …
classifier ensembles are either inconclusive, too heavily assumption-bound or openly non …
Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images
Observing and identifying blood cells is a direct way for early diagnosis of blood diseases.
Traditional blood cell recognition methods are usually time-consuming and laborious tasks …
Traditional blood cell recognition methods are usually time-consuming and laborious tasks …
An empirical study of dynamic selection and random under-sampling for the class imbalance problem
SM Liu, JH Chen, Z Liu - Expert Systems with Applications, 2023 - Elsevier
A detailed and extensive empirical study of dynamic selection (DS) and random under-
sampling (RUS) for the class imbalance problem is conducted in this paper. Total 20 state of …
sampling (RUS) for the class imbalance problem is conducted in this paper. Total 20 state of …
Predicting software reliability with neural network ensembles
J Zheng - Expert systems with applications, 2009 - Elsevier
Software reliability is an important factor for quantitatively characterizing software quality and
estimating the duration of software testing period. Traditional parametric software reliability …
estimating the duration of software testing period. Traditional parametric software reliability …