scholarly journals Impact of the normalisation method on the results of classification of decision variants by means of the generalised distance MEASURE

2017 ◽  
Vol 50 ◽  
pp. 7-18
Author(s):  
Krzysztof Dmytrów
Keyword(s):  
Distance Measure ◽  
Normalisation Method

scholarly journals Automatic Solder Joint Defect Classification using the Log-Gabor Filter

2010 ◽  
Vol 97-101 ◽  
pp. 2940-2943 ◽  
Author(s):  
Nang Seng Siri Mar ◽  
Clinton Fookes ◽  
K.D.V. Yarlagadda Prasad
Keyword(s):  
Solder Joint ◽  
Manufacturing Industry ◽  
Printed Circuit Boards ◽  
Distance Measure ◽  
Gabor Filter ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Different Types ◽  
Gabor Filter Bank

This paper proposes the validity of a Gabor filter bank for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance measure based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry.

scholarly journals Causality Distance Measures for Multivariate Time Series with Applications

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2708
Author(s):  
Achilleas Anastasiou ◽  
Peter Hatzopoulos ◽  
Alex Karagrigoriou ◽  
George Mavridoglou
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Multivariate Time Series ◽  
Health Resources ◽  
Unsupervised Classification ◽  
Distance Measures ◽  
Series Data ◽  
Multivariate Statistical

In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

scholarly journals An Investigation on Land Cover Mapping Capability of Classical and Fuzzy based Maximum Likelihood Classifiers

2018 ◽  
Vol 7 (2) ◽  
pp. 939 ◽  
Author(s):  
Shivakumar B R ◽  
Rajashekararadhya S V
Keyword(s):  
Maximum Likelihood ◽  
Land Cover ◽  
Classification Accuracy ◽  
Distance Measure ◽  
Accuracy Assessment ◽  
Sensor Data ◽  
Remote Sensor ◽  
Mixed Pixels ◽  
Overlapping Classes

In the past two decades, a significant amount of research has been conducted in the area of information extraction from heterogeneous remotely sensed (RS) datasets. However, it is arduous to exactly predict the behaviour of the classification technique employed due to issues such as the type of the dataset, resolution of the imagery, the presence of mixed pixels, and spectrally overlapping of classes. In this paper, land cover classification of the heterogeneous dataset using classical and Fuzzy based Maximum Likelihood Classifiers (MLC) is presented and compared. Three decision parameters and their significance in pixel assignment is illustrated. The presented Fuzzy based MLC uses a weighted inverse distance measure for defuzzification process. 10 pixels were randomly selected from the study area to illustrate pixel assignment for both the classifiers. The study aims at enhancing the classification accuracy of heterogeneous multispectral remote sensor data characterized by spectrally overlapping classes and mixed pixels. The study additionally aims at obtaining classification results with a confidence level of 95% with ±4% error margin. Classification success rate was analysed using accuracy assessment. Fuzzy based MLC produced significantly higher classification accuracy as compared to classical MLC. The conducted research achieves the expected classification accuracy and proves to be a valuable technique for classification of heterogeneous RS multispectral imagery. 

Observation Depth Measure in a Sample in the Voivodeship Classification of the Property Market

2013 ◽  
Vol 8 (4) ◽  
pp. 373-382
Author(s):  
Małgorzata Kobylińska
Keyword(s):  
Distance Measure ◽  
Property Market ◽  
Homogeneous Groups ◽  
Selection Of

The problem of classification has long been an object of interest in many fields of knowledge. It allows homogeneous groups of objects to be obtained with respect to a given criterion. The selection of the appropriate distance measure, which is used in the clustering of multivariate objects, has an important effect on the obtained classification results. This paper uses observation depth measure in a sample ofvoivodeship classification, with respect to selected features concerning the property market in 2011. Voivodeships characterized by typical values for all analysed features were distinguished and those which could be considered outliers were so designated because of high or low values for the studied variables. 

scholarly journals Prototype-based Models for the Supervised Learning of Classification Schemes

2016 ◽  
Vol 12 (S325) ◽  
pp. 129-138
Author(s):  
Michael Biehl ◽  
Barbara Hammer ◽  
Thomas Villmann
Keyword(s):  
Vector Quantization ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
High Dimensional ◽  
Main Concept ◽  
Classification Schemes ◽  
Relevance Learning

AbstractAn introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

Scale-Space Representation and Classification of 3D Models

2003 ◽  
Vol 3 (4) ◽  
pp. 315-324 ◽  
Author(s):  
Dmitriy Bespalov and ◽  
Ali Shokoufandeh ◽  
William C. Regli ◽  
Wei Sun
Keyword(s):  
Shape Matching ◽  
Distance Measure ◽  
Empirical Evaluation ◽  
3D Models ◽  
Scale Space ◽  
Space Representation ◽  
Point Distribution ◽  
Solid Models ◽  
Recent Developments

This paper presents a framework for shape matching and classification through scale-space decomposition of 3D models. The algorithm is based on recent developments in efficient hierarchical decomposition of a point distribution in metric space p,d using its spectral properties. Through spectral decomposition, we reduce the problem of matching to that of computing a mapping and distance measure between vertex-labeled rooted trees. We use a dynamic programming scheme to compute distances between trees corresponding to solid models. Empirical evaluation of the algorithm on an extensive set of 3D matching trials demonstrates both robustness and efficiency of the overall approach. Lastly, a technique for comparing shape matchers and classifiers is introduced and the scale-space method is compared with six other known shape matching algorithms.

scholarly journals Taxonomic Significance of Glume Morphology and Leaf Epidermal Characteristics in some Taxa of Tribe Aveneae (Poaceae)

2013 ◽  
Vol 5 (2) ◽  
pp. 144-155 ◽  
Author(s):  
Adel EL-GAZZAR ◽  
Monier El-GHANI ◽  
Lamiaa SHALABI
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Data Matrix ◽  
Numerical Classification ◽  
Taxonomic Significance ◽  
Clustering Method ◽  
Euclidean Distance Measure ◽  
Pca Method ◽  
Silica Skeleton

The numerical classification of tribe Aveneae (Poaceae) is discussed regarding the glume morphology and silica skeleton morphologies. The present study dealt with 18 species belonging to 10 genera of the tribe to cover as many groups as possible within Aveneae. The total of 31 structural characters and 71 character states were scored comparatively. The resulted data matrix was analyzed under a combination of Euclidean distance measure and Ward’s clustering method included in the program package PC-ORD version 5. The resulted dendrogram separated the tribe into five basic sub-ordinate groups created from three major groups A, B and C. The taxonomic significance of these results was discussed. The results showed congruence between the clustering and PCA method, in suggesting three major groups and 5 sub-ordinate groups.

scholarly journals Classification of the Leguminosae-Papilionoideae: A Numerical Re-assessment

2013 ◽  
Vol 5 (4) ◽  
pp. 499-507 ◽  
Author(s):  
Adel EL-GAZZAR ◽  
Monier EL-GHANI ◽  
Nahed EL-HUSSEINI ◽  
Adel KHATTAB
Keyword(s):  
Cluster Analysis ◽  
Distance Measure ◽  
Morphological Characters ◽  
Data Matrix ◽  
Clustering Method ◽  
Lower Rank ◽  
Phylogenetic Studies

The subdivision of the Leguminosae-Papilionoideae into taxa of lower rank was subject for major discrepancies between traditional classifications while more recent phylogenetic studies provided no decisive answer to this problem. As a contribution towards resolving this situation, 81 morphological characters were recorded comparatively for 226 species and infra-specific taxa belonging to 75 genera representing 21 of the 32 tribes currently recognized in this subfamily. The data matrix was subjected to cluster analysis using the Sørensen distance measure and Ward’s clustering method of the PC-ord version-5 package of programs for Windows. This combination was selected from among the 56 combinations available in this package because it produced the taxonomically most feasible arrangement of the genera and species. The 75 genera are divided into two main groups A and B, whose recognition requires little more than the re-alignment of a few genera to resemble tribes 1-18 (Sophoreae to Hedysareae) and tribes 19-32 (Loteae to Genisteae), respectively, in the currently accepted classification. Only six of the 21 tribes represented by two or more genera seem sufficiently robust as the genera representing each of them hold together in only one of the two major groups A and B. Of the 29 genera represented by more than one species each 17, 7 and 5 are taxonomically coherent, nearly coherent and incoherent, respectively. The currently accepted circumscription and inter-relationships among the disrupted tribes and genera are in need of much detailed investigation.

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