Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

Kıranyaz, Serkan; Gabbouj, Moncef; Pulkkinen, Jenni; İnce, Türker; Meissner, Kristian

doi:10.1109/icip.2010.5651161

Cited by 17 publications

(14 citation statements)

References 9 publications

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“…Additionally, there are other measures derived from Pol-SAR data such as three complex correlation coefficients (ρ 12 , ρ 13 , ρ 23 ) between scattering matrix terms that can be included in the FV to be used as the input to the CNBC framework. As a result, we formed the following three set of FVs (FV n ), which will be input (sub)features for the proposed network of BCs that will be detailed in Section IV.…”

Section: A Feature Extraction and Normalizationmentioning

confidence: 99%

“…In this paper, two common ANN types, the multilayer perceptrons (MLPs) and the radial basis function (RBF) networks are used as the BCs; however, any other classifier types such as support vector machines and random forests can also be used within CNBC framework as the BC type as long as they can be evolved incrementally. A basic CNBC topology has first been introduced in [23] for macro invertebrate classification using MLPs as the BC type. In addition to the exhaustive search with the numerous runs of the back-propagation (BP) method, the recently proposed multidimensional particle swarm optimization (MD-PSO) [24], [25] is used as the primary evolution technique.…”

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confidence: 99%

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Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach

Kıranyaz

İnce

Uhlmann

et al. 2012

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.Index Terms-Evolutionary classifiers, multidimensional particle swarm optimization (MD-PSO), polarimetric synthetic aperture radar (SAR).

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Section: A Feature Extraction and Normalizationmentioning

confidence: 99%

mentioning

confidence: 99%

Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach

Kıranyaz

İnce

Uhlmann

et al. 2012

IEEE Trans. Syst., Man, Cybern. B

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“…Currently, identification process is made manually by biologists or taxonomists. Automated benthic macroinvertebrate identification [6]- [10], [12]- [14], [16], [17], [19], [20] has gained a scant attention among computer scientists, but it can save resources and enable wider and more efficient biomonitoring.…”

Section: Introductionmentioning

confidence: 99%

Half-against-half structure in classification of benthic macroinvertebrate images

Joutsijoki

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Benthic macroinvertebrates play a key role when water quality assessments are made. Benthic macroinvertebrates are difficult to identify and their identification need special expertise. Furthermore, manual identification is slow and expensive process. This paper concerns benthic macroinverte-brate classification when Half-Against-Half (HAH) structure was applied to Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Minimum Mahalanobis Distance Classifier (MMDC) classifiers. Especially, LDA, QDA and MMDC classifiers were for first time applied with HAH structure to benthic macroinvertebrate classification. We performed thorough experiments altogether with ten methods. In the case of HAH-SVM we managed to improve classification results from the earlier research by using a different approach to class division problem. We obtained 96.1% classification accuracy with Radial Basis Function (RBF) kernel. Moreover, new variants of LDA, QDA and MMDC classification methods achieved 89.5% and 91.6% classification accuracies which can be considered as a good result in such a difficult classification task.

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“…The main idea of the framework was first introduced in a previous work [12], for another application area, being now specifically designed for audio classification purpose. Earlier, fundamentally similar type of approaches of constructing an ensemble of neural networks (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

Content-based audio classification using collective network of binary classifiers

Mäkinen

Kıranyaz

Gabbouj

2011

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

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Abstract-In this paper, a novel collective network of binary classifiers (CNBC) framework is presented for content-based audio classification. The topic has been studied in several publications before, but in many cases the number of different classification categories is quite limited and needed to be fixed a priori. We focus our efforts to increase both the classification accuracy and the number of classes, as well as to create a scalable network design, which allows introducing new audio classes incrementally. The approach is based on dividing a major classification problem into several networks of binary classifiers (NBCs), where each NBC adapts its internal topology according to the classification problem at hand, by using evolutionary Artificial Neural Networks (ANNs). In the current work, feedforward ANNs, or the so-called Multilayer Perceptrons (MLPs), are evolved within an architecture space, where a stochastic optimization is applied to seek for the optimal classifier configuration and parameters. The performance evaluations of the proposed framework over an 8-class benchmark audio database demonstrate its scalability and notable potential, as classification error rates of less than 9% are achieved.

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Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

Cited by 17 publications

References 9 publications

Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach

Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach

Half-against-half structure in classification of benthic macroinvertebrate images

Content-based audio classification using collective network of binary classifiers

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