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Classifying Clustering Schemes [article]

Gunnar Carlsson, Facundo Memoli
2010 arXiv   pre-print
By varying the degree of functoriality that one requires from the schemes it is possible to construct richer families of clustering schemes that exhibit sensitivity to density.  ...  Many clustering schemes are defined by optimizing an objective function defined on the partitions of the underlying set of a finite metric space.  ...  The point of the present paper is to illustrate that functoriality allows a very useful framework for classifying large families of clustering schemes.  ... 
arXiv:1011.5270v2 fatcat:7va3laj5dbhp3bctdeqhd26bzq

A New Classifier Combination Scheme Using Clustering Ensemble [chapter]

Miguel A. Duval-Poo, Joan Sosa-García, Alejandro Guerra-Gandón, Sandro Vega-Pons, José Ruiz-Shulcloper
2012 Lecture Notes in Computer Science  
The proposed scheme uses the same concept of representing the classifiers decision as a vector in an intermediate feature space and builds more representatives decision templates by using clustering ensembles  ...  In this paper we introduce a new classifier combination scheme which is based on the Decision Templates Combiner.  ...  Conclusions In this paper, we have proposed a new scheme to combine multiple classifiers by using clustering ensemble.  ... 
doi:10.1007/978-3-642-33275-3_19 fatcat:txz744kwwbdnpfgpsnlwb4xyfa

Assessing Visual Field Clustering Schemes Using Machine Learning Classifiers in Standard Perimetry

Catherine Boden, Kwokleung Chan, Pamela A. Sample, Jiucang Hao, Te-Wan Lee, Linda M. Zangwill, Robert N. Weinreb, Michael H. Goldbaum
2007 Investigative Ophthalmology and Visual Science  
To compare machine learning classifiers trained on three clustering schemes to determine whether distinguishing healthy eyes from those with glaucomatous optic neuropathy (GON) can be optimized by training  ...  Innovations in Glaucoma Study (DIGS), clustered using three clustering schemes on a training data set (123 eyes/123 glaucoma patients with GON; 135 eyes/135 normal control subjects).  ...  be optimized by training with clustered data; (2) which MLC, visual field mapping scheme or MLC/map combination achieves the highest performance; and (3) how structure-derived schemes compare to the  ... 
doi:10.1167/iovs.06-0897 pmid:18055807 pmcid:PMC2647327 fatcat:vkrgne3p7jhnjd6kovdnn5hwv4

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System

Shu-Zhi Liu, Rashmi Sharan Sinha, Seung-Hoon Hwang
2021 Sensors  
The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise.  ...  To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets.  ...  The pre-processing of RSSI data with a clustering-based noise elimination scheme (CNES) is a novel concept.  ... 
doi:10.3390/s21134349 pmid:34202090 fatcat:2ywrhp76q5hmnaettszu4bktau

Classifier scheme for clustered microcalcifications in digitized mammograms by using Artificial Neural Networks

Ana Claudia Patrocinio, Homero Schiabel
2016 Anais do 5. Congresso Brasileiro de Redes Neurais   unpublished
The classifier using ANN has shown the geometric descriptors efficiency for characterizing microcalcifications clusters as well as the influence of features extracted from images reports, as "age" and  ...  Computer-Aided Diagnosis (CAD) schemes have presented good results in aiding the early diagnosis of breast cancer.  ...  As part of a mammography CAD scheme, our purpose in this work was to develop a classifier based on ANN in order to classify clustered microcalcifications as "suspect" and "non suspect" regarding a better  ... 
doi:10.21528/cbrn2001-123 fatcat:2fdinqftfjdihhhp5mu7rrvhry

The implication of data diversity for a classifier-free ensemble selection in random subspaces

Albert Hung-Ren Ko, Robert Sabourin, Luiz E. Soares de Oliveira, Alceu de Souza Britto
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
Ensemble of Classifiers (EoC) has been shown effective in improving the performance of single classifiers by combining their outputs.  ...  By using diverse data subsets to train classifiers, the ensemble creation methods can create diverse classifiers for the EoC.  ...  The clustering diversity measures yielded encouraging performances as objective functions for the classifier-free ensemble selection scheme.  ... 
doi:10.1109/icpr.2008.4761767 dblp:conf/icpr/KoSOB08 fatcat:6ph4ssditvgvvnjdzcfswjvxoy

Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling

Aytuğ Onan
2018 Computational and Mathematical Methods in Medicine  
performance) from each cluster is selected to build the final multiple classifier system.  ...  In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined.  ...  In this scheme, classifiers are assigned into clusters based on their predictive performance and the set of candidate classifiers are explored through the use of evolutionary algorithm.  ... 
doi:10.1155/2018/2497471 pmid:30140300 pmcid:PMC6081524 fatcat:7nogizhvsfaxvah5y5f6g3ac4q

A Combination Of Classical And Fuzzy Classification Techniques On A Self Organized Memories (Som).-Type Neural Network Computational Platform

Spyros G. Tzafestas, Sotiris N. Raptis
1998 Zenodo  
Thus for each pattern to classify a set of predefined clusters is prescribed.  ...  Furthermore, in the second part three classifiers are compared in terms of their performance within the framework of the general two parts scheme, namely the F.C.M. classifier, the classical N.N.R. classifier  ... 
doi:10.5281/zenodo.36924 fatcat:jgedfq4lo5boffc2jhifngdcxq

A multi-schematic classifier-independent oversampling approach for imbalanced datasets [article]

Saptarshi Bej, Kristian Schultz, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer
2021 arXiv   pre-print
Most importantly, the performance of ProWRAS with proper choice of oversampling schemes, is independent of the classifier used.  ...  ProWRAS has four oversampling schemes, each of which has its unique way to model the variance of the generated data.  ...  ← cluster, weight weight_sum : (cluster, weight) ∈ clusters Returns pairs of clusters and normalized weights as clusters. end Algorithm 3 Cluster-wise oversampling schemes Inputs: cluster Data points  ... 
arXiv:2107.07349v1 fatcat:6awj5ffi2fe7zhycem4iletu4e

Temporal and spatial approaches for land cover classification

Ryabukhin Sergey
2017 European Conference on Principles of Data Mining and Knowledge Discovery  
Also the di↵erent cross-validation schemes considered to evaluate performance of approaches.  ...  Using features extracted from satellite images time series (SITS) each pixel corresponding to 30m⇥30m area can be classified to one of general class (urban area, forest, water, etc.).  ...  Table 1 . 1 F1score weighted on di↵erent approaches (rows) and cross-validation schemes (columns) Method Classifier Original Rectangles Train 70% Train 40% Train 10% Benchmark ETC30 0.8893 0.7797 0.8824  ... 
dblp:conf/pkdd/Sergey17 fatcat:x4pc5gwoivbjlb4ojnoojbwi3q

Categorization of Event Clusters from Twitter Using Term Weighting Schemes

Surender Singh Samant, NL Bhanu Murthy, Aruna Malapati
2021 Informatica (Ljubljana, Tiskana izd.)  
In this paper, we propose a new term weighting scheme and a modification to an existing one and compare them with many state-of-the-art methods using three popular classifiers.  ...  We create two majority voting based classifiers that further enhance the F1-scores of the best individual schemes. Povzetek: V prispevku je opisana kategorizacija gruč dogodkov na Twitterju.  ...  In a voting classifier, the category label of an event cluster is selected by majority decision among the three schemes (i.e. each cluster's category prediction is common to at least two of the schemes  ... 
doi:10.31449/inf.v45i3.3063 fatcat:bhxm5fp7wjavnolzuawethylam

Improving a dynamic ensemble selection method based on oracle information

Leila Maria Vriesmann, Alceu De Souza Britto Jr., Luiz Eduardo Soares De Oliveira, Robert Sabourin, Albert Houng Ren Ko
2012 International Journal of Innovative Computing and Applications  
information calculated by using a clustering process in the validation dataset.  ...  This work evaluates some strategies to approximate the performance of a dynamic ensemble selection method to the oracle performance of its pool of weak classifiers.  ...  using information about class accuracy were evaluated: a statistics related to the classifier accuracy (ranking of classifiers) for each class b statistics related to a clustering scheme, where each test  ... 
doi:10.1504/ijica.2012.050053 fatcat:qxgh7znfuzabjbz7r4kftndbpi

Adaptive Fuzzy Clustering

Nicolas Cebron, Michael R. Berthold
2006 NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society  
We propose a new adaptive active clustering scheme, based on an initial Fuzzy c-means clustering and Learning Vector Quantization.  ...  This scheme can initially cluster large datasets unsupervised and then allows for adjustment of the classification by the user.  ...  We propose a new adaptive active clustering scheme, based on an initial Fuzzy c-means clustering and Learning Vector Quantization.  ... 
doi:10.1109/nafips.2006.365406 fatcat:e7pd7czu7vgidldn7an4poqdeq

Use of Word Clustering to Improve Emotion Recognition from Short Text

Shuai Yuan, Huan Huang, Linjing Wu
2016 Journal of Computing Science and Engineering  
features, offering a novel word clustering algorithm, and using a new feature weighting scheme.  ...  The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.  ...  weighting scheme, the emotion classifier performs better for most of the specific emotions; and 3) using word cluster features and the proposed weighting scheme can also improve the whole performance  ... 
doi:10.5626/jcse.2016.10.4.103 fatcat:7w3l2phjhrgadiuvhmwpyqxgae

Distributed and efficient classifiers for wireless audio-sensor networks

Baljeet Malhotra, Ioanis Nikolaidis, Mario A. Nascimento
2008 2008 5th International Conference on Networked Sensing Systems  
The features generated through our proposed schemes are evaluated using K-Nearest Neighbor ( -NN) and Maximum Likelihood (ML) classifiers.  ...  Cluster head Cluster  ...  for IFS scheme in the ML classifier.  ... 
doi:10.1109/inss.2008.4610886 fatcat:szyowgy54vc2dlnqjnfp2g2s64
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