Dynamic classifier selection: Recent advances and perspectives

Cruz, Rafael M. O.; Sabourin, Robert; Cavalcanti, George D. C.

doi:10.1016/j.inffus.2017.09.010

Cited by 319 publications

(226 citation statements)

References 102 publications

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“…22. An alternative to Stacking is the Dynamic Selection algorithm, which uses only the most competent classifier or ensemble to predict the class of a sample, rather than combining the predictions [45].…”

Section: Ensemble Learning: Gradient Boosting Machines and Model Combmentioning

confidence: 99%

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

2020

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Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

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Section: Ensemble Learning: Gradient Boosting Machines and Model Combmentioning

confidence: 99%

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

2020

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“…Differently from the literature on DCS, where several competence measures have been proposed and assessed during the last decade [5], [1], the number of works dealing with DRS is quite limited. The central issue in DRS, i.e., defining competence measures to help selecting the best regressor or ensemble of regressors, has been neglected in most of the works.…”

Section: Discussionmentioning

confidence: 99%

“…Model selection systems consist of two main phases [1]: Generation and Selection. In the first phase, a training set is used to generate the ensemble.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Competence Measures for Dynamic Regressor Selection

Moura

Cavalcanti

Oliveira

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is usually quantified using the performance of the regressors in local regions of the feature space around the test pattern. However, choosing the best measure to calculate the level of competence correctly is not straightforward. The literature of dynamic classifier selection presents a wide variety of competence measures, which cannot be used or adapted for DRS. In this paper, we review eight measures used with regression problems, and adapt them to test the performance of the DRS algorithms found in the literature. Such measures are extracted from a local region of the feature space around the test pattern, called region of competence, therefore competence measures. To better compare the competence measures, we perform a set of comprehensive experiments of 15 regression datasets. Three DRS systems were compared against individual regressor and static systems that use the Mean and the Median to combine the outputs of the regressors from the ensemble. The DRS systems were assessed varying the competence measures. Our results show that DRS systems outperform individual regressors and static systems but the choice of the competence measure is problem-dependent.

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“…Classifier generation has the aim of producing accurate yet diverse classifiers and has well established methods [5] although diversity is still an elusive concept [6]. Classifier selection is being actively researched [7] and either a single classifier or ensemble subset can be selected; selection may be static which is based on the training/validation dataset or dynamic, in which selection differs for each test pattern.…”

Section: Introductionmentioning

confidence: 99%

“…Classifier selection is being actively researched [7] and either a single classifier or ensemble subset can be selected; selection may be static which is based on the training/validation dataset or dynamic, in which selection differs for each test pattern. While dynamic selection may improve generalisation, it has the disadvantage of introducing extra parameters to define the region of competence, competence criterion and selection strategy [5].…”

Section: Introductionmentioning

confidence: 99%

Approximation of Ensemble Boundary Using Spectral Coefficients

Windeatt

Zor

Camgöz

2019

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-A spectral analysis of a Boolean function is proposed for approximating the decision boundary of an ensemble of classifiers, and an intuitive explanation of computing Walsh coefficients for the functional approximation is provided. It is shown that the difference between first and third order coefficient approximation is a good indicator of optimal base classifier complexity. When combining Neural Networks, experimental results on a variety of artificial and real two-class problems demonstrate under what circumstances ensemble performance can be improved. For tuned base classifiers, first order coefficients provide performance similar to majority vote. However, for weak/fast base classifiers, higher order coefficient approximation may give better performance. It is also shown that higher order coefficient approximation is superior to the Adaboost logarithmic weighting rule when boosting weak Decision Tree base classifiers.

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Dynamic classifier selection: Recent advances and perspectives

Cited by 319 publications

References 102 publications

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Evaluating Competence Measures for Dynamic Regressor Selection

Approximation of Ensemble Boundary Using Spectral Coefficients

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