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Dynamic ensemble selection of convolutional neural networks and its application in flower classification
2021
International Journal of Agricultural and Biological Engineering
Second, the thirteen classifiers were sorted by a classifier sorting algorithm, before ensemble selection, to avoid an exhaustive search. ...
To solve these problems and improve the accuracy of flower classification, the advantages of CNNs were combined with those of ensemble learning and a method was developed for the dynamic ensemble selection ...
Dynamic classifier ensemble selection After all base classifiers are sorted, an optimal classifier subset is selected dynamically from the sorted classifier sequence P and integrated to identify the test ...
doi:10.25165/j.ijabe.20221501.6313
fatcat:pyd4qwdrq5bhvhwjirlacdr5g4
Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy
2021
Entropy
the dynamic selection of base classifiers in classification. ...
for increasing the diversity of base classifiers. ...
Specifically, the selection structure for base classifiers was a key component in their dynamic selection model. ...
doi:10.3390/e23070822
fatcat:xxyob5zr6ffsbjjfgfy6a77y2u
A dynamic overproduce-and-choose strategy for the selection of classifier ensembles
2008
Pattern Recognition
It is therefore, a static classifier ensemble selection strategy. ...
The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble ...
selection level Name
Label
↑ / ↓
Ambiguity-guided dynamic selection
ADS
(↓)
Margin-based dynamic selection
MDS
(↑)
Class strength-based dynamic selection
CSDS
(↑)
Dynamic ensemble selection ...
doi:10.1016/j.patcog.2008.03.027
fatcat:wuglpp5ojfbbnkukd2zalbx7ku
HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets
2020
Computational Intelligence and Neuroscience
Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. ...
In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. ...
ere are two general approaches for selecting the base classifiers of an ensemble classifier: static approaches and dynamic approaches. ...
doi:10.1155/2020/8826914
pmid:33488690
pmcid:PMC7803144
fatcat:vu5a55bktndytniudmyv2agzxa
An automatic construction and organization strategy for ensemble learning on data streams
2006
SIGMOD record
Currently, the typical approach to this problem is based on ensemble learning, which learns basic classifiers from training data stream and forms the global predictor by organizing these basic ones. ...
In this paper, we propose an ensemble learning algorithm, which: (1) furnishes training data for basic classifiers, starting from the up-to-date data chunk and searching for complement from past chunks ...
recent N basic classifiers as the ensemble; (3) for each test point, we use the ensemble to classify the data based on globalprediction strategy. ...
doi:10.1145/1168092.1168096
fatcat:xulya6qld5h73laegrydztswju
Dynamic classifier ensemble using classification confidence
2013
Neurocomputing
How to combine the outputs from base classifiers is a key issue in ensemble learning. This paper presents a dynamic classifier ensemble method termed as DCE-CC. ...
It dynamically selects a subset of classifiers for test samples according to classification confidence. ...
Inspired by the idea of dynamic classifier selection, we propose a dynamic classifier ensemble method in this paper based on the classification confidence of the test sample (termed as DCE-CC). ...
doi:10.1016/j.neucom.2012.07.026
fatcat:6ygpuinbpvbbpo26wvuvphky2u
K-Nearest Oracle for Dynamic Ensemble Selection
2007
Proceedings of the International Conference on Document Analysis and Recognition
One of the most important issues to optimize a multiple classifier system is to select a group of adequate classifiers, known as Ensemble of Classifiers (EoC), from a pool of classifiers. ...
Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. ...
Based on this reason, we tested the MAJ as the objective function for the ensemble selection. Furthermore, we tested the mean classifier error (ME) as well. ...
doi:10.1109/icdar.2007.4378744
dblp:conf/icdar/KoSB07
fatcat:23jf7s2vhffdrcwaumrngyulga
Adaptive Incremental Learning with an Ensemble of Support Vector Machines
2010
2010 20th International Conference on Pattern Recognition
The key idea is to track, evolve, and combine optimum hypotheses over time, based on dynamic optimization processes and ensemble selection. ...
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. ...
selects ensembles based on the classifiers' confidence levels to improve the overall results. ...
doi:10.1109/icpr.2010.984
dblp:conf/icpr/KappSM10
fatcat:7pbkqozu3rc7blkjmlcys73ow4
Visualization and classification of physiological failure modes in ensemble hemorrhage simulation
2015
Visualization and Data Analysis 2015
The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data ...
to existing ensembles. ...
Hierarchical clustering with dynamic time warping (Figure 6 (e,f)), on the other hand, classified the two groups of virtual patients who died based on their heart rate patterns before the failure. ...
doi:10.1117/12.2080136
dblp:conf/vda/0004PH15
fatcat:37n2yfbtmfhv3ilw63k3ael7le
A Dynamic Two-Layers MI and Clustering-based Ensemble Feature Selection for Multi-Labels Text Classification
2020
International Journal of Advanced Computer Science and Applications
In addition, this paper proposes an enhanced FS method called dynamic multi-label two-layers MI and clusteringbased ensemble feature selection algorithm (DMMC-EFS). ...
The proposed method considers the: 1) dynamic global weight of feature, 2) heterogeneous ensemble, and 3) maximum dependency and relevancy and minimum redundancy of features. ...
Second Ensemble Layer of the Dynamic MI-based Multi-Label Feature Selection Algorithm In the second ensemble layer, the ensemble FS method [17] takes into account the dynamic change of the selected features ...
doi:10.14569/ijacsa.2020.0110764
fatcat:uj4mbpalqfhzljp6b6fcq4pcgu
A ROBUST TECHNIQUE OF FAKE NEWS IDENTIFICATION USING ENSEMBLE FEATURE SELECTION
2021
Indian Journal of Computer Science and Engineering
Therefore, this work implements a Robust Framework of Ensemble feature selection Technique. ...
Though it performs well, sometimes they are not helpful in learning the model by selecting single feature with single classifier. This overfits the model and leads to unnecessary confusion. ...
In 2017, [14] proposed a unique ensemble-based feature selection technique using a bi-objective evolutionary algorithm and a dynamic mating pool. ...
doi:10.21817/indjcse/2021/v12i6/211206154
fatcat:hvdz2ocqmnfalkewxq7626hj4q
From dynamic classifier selection to dynamic ensemble selection
2008
Pattern Recognition
One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. ...
Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. ...
(2) Can dynamic ensemble selection outperform dynamic classifier selection? (3) Can dynamic ensemble selection outperform static ensemble selection? ...
doi:10.1016/j.patcog.2007.10.015
fatcat:w7macooplrgudbdz5areqvnmam
HAR-MI method for multi-class imbalanced datasets
2020
TELKOMNIKA (Telecommunication Computing Electronics and Control)
out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. ...
In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried ...
In [12] suggested the Dynamic Ensemble Selection (DES)-MI method which gives better results compared to the Dynamic Classifier Selection (DCS) method. ...
doi:10.12928/telkomnika.v18i2.14818
fatcat:pebiovyzibgwvj42nvsn6vchde
Combining Machine Learning Models Using combo Library
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. ...
Selected classifier combination methods implemented in combo include stacking (meta-learning), dynamic classifier selection, dynamic ensemble selection, and a group of heuristic aggregation methods like ...
Firstly, combo contains more than 15 combination algorithms, including both classical algorithms like dynamic classifier selection (DCS) (1997) and recent advancement like LCSP (2019). ...
doi:10.1609/aaai.v34i09.7111
fatcat:hcbv4ldcmbg5jgzjvcuimt3mm4
Combining Machine Learning Models using combo Library
[article]
2019
arXiv
pre-print
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. ...
Selected classifier combination methods implemented in combo include stacking (meta-learning), dynamic classifier selection, dynamic ensemble selection, and a group of heuristic aggregation methods like ...
Firstly, combo contains more than 15 combination algorithms, including both classical algorithms like dynamic classifier selection (DCS) (1997) and recent advancement like LCSP (2019) . ...
arXiv:1910.07988v2
fatcat:vv25ofpzknf77eb6w7wwxizuva
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