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 …
Ensemble methods
ZH Zhou - Combining pattern classifiers. Wiley, Hoboken, 2014 - api.taylorfrancis.com
Ensemble methods that train multiple learners and then combine them for use, with Boosting
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …
A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches
R Mousavi, M Eftekhari - Applied Soft Computing, 2015 - Elsevier
Ensemble learning is a system that improves the performance and robustness of the
classification problems. How to combine the outputs of base classifiers is one of the …
classification problems. How to combine the outputs of base classifiers is one of the …
Combining classifiers: Soft computing solutions
LI Kuncheva - Pattern recognition: From classical to modern …, 2001 - World Scientific
Classifier combination is now an established pattern recognition subdiscipline. Despite the
strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and …
strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and …
Ensemble pruning based on objection maximization with a general distributed framework
Ensemble pruning, selecting a subset of individual learners from an original ensemble,
alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and …
alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and …
Vision-based perception and classification of mosquitoes using support vector machine
The need for a novel automated mosquito perception and classification method is becoming
increasingly essential in recent years, with steeply increasing number of mosquito-borne …
increasingly essential in recent years, with steeply increasing number of mosquito-borne …
Long-term impact of rapid urbanization on urban climate and human thermal comfort in hot-arid environment
SH Mahmoud, TY Gan - Building and Environment, 2018 - Elsevier
The long-term impact of rapid urbanization on air temperature (T a), relative humidity (RH),
vapor pressure (VP) and human thermal comfort in the Cairo governorate of Egypt was …
vapor pressure (VP) and human thermal comfort in the Cairo governorate of Egypt was …
Classification of similar sports images using convolutional neural network with hyper-parameter optimization
V Podgorelec, Š Pečnik, G Vrbančič - Applied Sciences, 2020 - mdpi.com
Featured Application The paper presents a method for the classification of sports disciplines
from images using fine-tuning of transfer learning for training a convolutional neural network …
from images using fine-tuning of transfer learning for training a convolutional neural network …
Mapping a specific class with an ensemble of classifiers
Often, in remote sensing, interest is focused on just one of the many classes that are typically
represented in the area covered by an image. Various binary classifiers may be used to …
represented in the area covered by an image. Various binary classifiers may be used to …
Using accuracy and diversity to select classifiers to build ensembles
Ensemble of classifiers is an effective way of improving performance of individual classifiers.
However, the task of selecting the ensemble members is often a non-trivial one. For …
However, the task of selecting the ensemble members is often a non-trivial one. For …