Differential Evolution Classifier with Optimized OWA-Based Multi-distance Measures for the Features in the Data Sets

2015 ◽  
pp. 765-777
Author(s):  
David Koloseni ◽  
Mario Fedrizzi ◽  
Pasi Luukka ◽  
Jouni Lampinen ◽  
Mikael Collan
Keyword(s):  
Differential Evolution ◽  
Distance Measures ◽  
Data Sets

A Novel Scalable Signature Based Subspace Clustering Approach for Big Data

2019 ◽  
Vol 14 (2) ◽  
pp. 41-51 ◽  
Author(s):  
T. Gayathri ◽  
D. Lalitha Bhaskari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Subspace Clustering ◽  
Distance Measures ◽  
Data Sets ◽  
Management Tools ◽  
Clustering Approach ◽  
Different Dimensions

“Big data” as the name suggests is a collection of large and complicated data sets which are usually hard to process with on-hand data management tools or other conventional processing applications. A scalable signature based subspace clustering approach is presented in this article that would avoid identification of redundant clusters. Various distance measures are utilized to perform experiments that validate the performance of the proposed algorithm. Also, for the same purpose of validation, the synthetic data sets that are chosen have different dimensions, and their size will be distributed when opened with Weka. The F1 quality measure and the runtime of these synthetic data sets are computed. The performance of the proposed algorithm is compared with other existing clustering algorithms such as CLIQUE.INSCY and SUNCLU.

Distance Measures for Surgical Process Models

2013 ◽  
Vol 52 (05) ◽  
pp. 422-431 ◽  
Author(s):  
U. Bühligen ◽  
T. Neumuth ◽  
S. Schumann
Keyword(s):  
Graph Matching ◽  
Surgical Techniques ◽  
Process Models ◽  
Distance Measures ◽  
Levenshtein Distance ◽  
Data Sets ◽  
Distance Measurements ◽  
Surgical Interventions ◽  
The Difference ◽  
The Impact

SummaryBackground: The development of new resources, such as surgical techniques and approaches, results in continuous modification of surgery. To assess these modifications, it is necessary to use measures that quantify the impact of resources on surgical processes.Objectives: The objective of this work is to introduce and evaluate distance measurements that are able to represent differences in the courses of surgical interventions as processes.Methods: Hence, we present four different distance measures for surgical processes: the Jaccard distance, Levenshtein distance, Adjacency distance, and Graph matching distance. These measures are formally introduced and evaluated by applying them to clinical data sets from laparoscopic training in pediatric surgery.Results: We analyzed the distances of 450 surgical processes using these four measures with a focus on the difference in surgical processes performed by novices and by experienced surgeons. The Levenshtein and Adjacency distances were best suited to measure distances between surgical processes.Conclusion: The measurement of distances between surgical processes is necessary to estimate the benefit of new surgical techniques and strategies.

Using differential evolution for improving distance measures of nominal values

2018 ◽  
Vol 64 ◽  
pp. 14-34 ◽  
Author(s):  
Diab M. Diab ◽  
Khalil El Hindi
Keyword(s):  
Differential Evolution ◽  
Distance Measures

scholarly journals An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 32
Author(s):  
Kiyoharu Tagawa
Keyword(s):  
Differential Evolution ◽  
Information Technologies ◽  
Flood Control ◽  
Stratified Sampling ◽  
Data Sets ◽  
Data Set ◽  
Relaxation Problem ◽  
Constrained Problems ◽  
Huge Data ◽  
Adaptive Differential Evolution

In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.

Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution

2008 ◽  
pp. 266-283
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Food Product ◽  
Data Sets ◽  
Objective Functions ◽  
Rank Position ◽  
Environmental Projects ◽  
Subsequent Improvement

This chapter investigates the modelling of the ability to improve the rank position of an alternative in relation to those of its competitors. PROMETHEE is one such technique for ranking alternatives based on their criteria values. In conjunction with the evolutionary algorithm Trigonometric Differential Evolution, the minimum changes necessary to the criteria values of an alternative are investigated, for it to achieve an improved rank position. This investigation is compounded with a comparison of the differing effects of two considered objective functions that measure the previously mentioned minimization. Two data sets are considered, the first concerns the ranking of environmental projects, and the second the ranking of brands of a food product. The notion of modelling preference ranks of alternatives and the subsequent improvement of alternative’s rank positions is the realism of a stakeholders’ appreciation of their alternative in relation to their competitors.

Statistical analysis of neural organization

1993 ◽  
Vol 70 (6) ◽  
pp. 2289-2300 ◽  
Author(s):  
R. P. Erickson ◽  
J. L. Rodgers ◽  
W. S. Sarle
Keyword(s):  
Empirical Data ◽  
Statistical Power ◽  
Distance Measures ◽  
Data Sets ◽  
Complex Data ◽  
Model Data ◽  
Taste System ◽  
Completely Regular ◽  
Neural Organization ◽  
And Cluster Analysis

1. The responses of 32 taste neurons in the solitary nucleus of the rat to 12 stimuli were analyzed with multidimensional scaling (MDS) and cluster analysis (CA) procedures. These analyses of empirical taste data were compared with similar analyses of two model data sets of known configuration to help clarify the implications of these methods commonly used in forming conclusions about the organization of the taste system. 2. To relate to possible conclusions about groupings in taste, both model data sets were chosen as the best possible examples of ungrouped data, the first being completely regular (in the form of a checkerboard) across the taste space, the second randomly arranged. The analysis of the present empirical data appear to be similar to the present ungrouped models, more so the random than the regular model, in the sense that all are amenable to grouping. 3. Because of the similarity of these model MDS and CA solutions to the present empirical solutions and to most published analyses of this sort, the idea is suggested that the appearance of the plots per se for empirical data does not support the conclusion of grouping. And, technically, MDS and CA do not have the statistical power to provide conclusions about issues of neural organization. 4. MDS and CA analyses have two very powerful roles relating to their ability to disclose the hidden organization of complex data sets; they may lend support for or refute theories about the data sets developed from other considerations, and may help generate theories for further consideration. The question of groupings is only one of many such issues. 5. Because data in the present and other reports are quite adequately accounted by MDS solutions of low dimensionality, it is suggested that their organization is characterized as continuous (i.e., rather than belonging to several disjoint spaces). 6. The use of correlations as distance measures in MDS and CA procedures distorts the spatial solutions, making analysis by visual inspection misleading. For example, using correlations, the true or natural spatial arrangements of data sets are probably less circular or spherical than shown in published MDS solutions. Also they are probably more evenly distributed across the space in the sense that the points are actually more concentrated toward the centers of the spaces; this may have strong influences on interpretations of the general form of the solutions. CA solutions can be influenced in analogous fashion. These problems of distortion of the solutions can be avoided with use of direct, linear estimates of distances. (ABSTRACT TRUNCATED AT 400 WORDS)

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