Dynamic classifier selection: Recent advances and perspectives
Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
How complex is your classification problem? a survey on measuring classification complexity
Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …
be effective predictors in a number of meta-analyses. Among them, measures of …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
A dynamic ensemble learning algorithm for neural networks
This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
The choice of scaling technique matters for classification performance
Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
A dataset for breast cancer histopathological image classification
Today, medical image analysis papers require solid experiments to prove the usefulness of
proposed methods. However, experiments are often performed on data selected by the …
proposed methods. However, experiments are often performed on data selected by the …
A comparative study on base classifiers in ensemble methods for credit scoring
J Abellán, JG Castellano - Expert systems with applications, 2017 - Elsevier
In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …
has meant an improvement over classic methods. Small improvements in the systems about …
Offline handwritten signature verification—Literature review
LG Hafemann, R Sabourin… - … conference on image …, 2017 - ieeexplore.ieee.org
The area of Handwritten Signature Verification has been broadly researched in the last
decades, but remains an open research problem. The objective of signature verification …
decades, but remains an open research problem. The objective of signature verification …
LSCP: Locally selective combination in parallel outlier ensembles
In unsupervised outlier ensembles, the absence of ground truth makes the combination of
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …
META-DES: A dynamic ensemble selection framework using meta-learning
Dynamic ensemble selection systems work by estimating the level of competence of each
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …