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Dynamic ensemble selection of convolutional neural networks and its application in flower classification

Zhibin Wang, 1. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China, Kaiyi Wang, Xiaofeng Wang, Shouhui Pan, Xiaojun Qiao, 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
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

Dongxue Zhao, Xin Wang, Yashuang Mu, Lidong Wang
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

Eulanda M. Dos Santos, Robert Sabourin, Patrick Maupin
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

Nasrin Ostvar, Amir Masoud Eftekhari Moghadam, Mario Versaci
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

Yi Zhang, Xiaoming Jin
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

Leijun Li, Bo Zou, Qinghua Hu, Xiangqian Wu, Daren Yu
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

A.H.-R. Ko, R. Sabourin, A. Britto Jr.
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

Marcelo N. Kapp, Robert Sabourin, Patrick Maupin
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

Song Zhang, William Andrew Pruett, Robert Hester, David L. Kao, Ming C. Hao, Mark A. Livingston, Thomas Wischgoll
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

Adil Yaseen Taha, Sabrina Tiun, Abdul Hadi, Masri Ayob, Ali Sabah
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

Sandrilla R., M. Savitha Devi
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

Albert H.R. Ko, Robert Sabourin, Alceu Souza Britto, Jr.
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

H. Hartono, Yeni Risyani, Erianto Ongko, Dahlan Abdullah
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

Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding
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]

Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding
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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