Improved distance measure for pattern recognition

1971 ◽  
Vol 7 (18) ◽  
pp. 521 ◽  
Author(s):  
B.G. Batchelor
Keyword(s):  
Pattern Recognition ◽  
Distance Measure

Ordered weighted logarithmic averaging distance-based pattern recognition for the recommendation of traditional Chinese medicine against COVID-19 under a complex environment

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuhe Fu ◽  
Chonghui Zhang ◽  
Yujuan Chen ◽  
Fengjuan Gu ◽  
Tomas Baležentis ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Recommendation System ◽  
Distance Measure ◽  
Uncertain Information ◽  
Content Type ◽  
Appropriate Treatment ◽  
Treatment Plans ◽  
Fuzzy Linguistic

PurposeThe proposed DHHFLOWLAD is used to design a recommendation system, which aims to provide the most appropriate treatment to the patient under a double hierarchy hesitant fuzzy linguistic environment.Design/methodology/approachBased on the ordered weighted distance measure and logarithmic aggregation, we first propose a double hierarchy hesitant fuzzy linguistic ordered weighted logarithmic averaging distance (DHHFLOWLAD) measure in this paper.FindingsA case study is presented to illustrate the practicability and efficiency of the proposed approach. The results show that the recommendation system can prioritize TCM treatment plans effectively. Moreover, it can cope with pattern recognition problems efficiently under uncertain information environments.Originality/valueAn expert system is proposed to combat COVID-19 that is an emerging infectious disease causing disruptions globally. Traditional Chinese medicine (TCM) has been proved to relieve symptoms, improve the cure rate, and reduce the death rate in clinical cases of COVID-19.

Application of Syntactic Pattern Recognition Approach in Design and Optimisation of Group Machining Systems

2010 ◽  
Vol 165 ◽  
pp. 342-347 ◽  
Author(s):  
Mieczyslaw Siemiatkowski
Keyword(s):  
Pattern Recognition ◽  
Group Process ◽  
Clustering Algorithm ◽  
Group Technology ◽  
Distance Measure ◽  
Cell Formation ◽  
Systems Design ◽  
Syntactic Pattern Recognition ◽  
Job Sequencing ◽  
Syntactic Pattern

The focus of this paper is on planning applications of group technology (GT) and the design of related layouts for multi-assortment cellular manufacturing (CM) of mechanical parts. A methodical approach is developed to optimally solve cell formation (CF) problems with CM systems design, which consists in the identification of machine cells and corresponding part families. The approach involves the use of syntactic pattern recognition concepts from the field of artificial intelligence (AI). It is based on methods of strings matching and clustering, applied extensively in genetics, molecular chemistry and biological sciences. The CF strategy followed implies clustering character strings that denote machine sequences in process routings. Numerical quantification of dissimilarity between part routings by a specific distance measure and the concept of average linkage clustering algorithm (ALCA) are at the core of the clustering procedure. The use of the approach is studied numerically with regard to a real industrial case and diverse layouts of cellular system are considered, including those with machine sharing. Group process alternatives with given system layouts and workflows prototyped by definite job sequencing rules, are simulated using programmed models. Generated design solutions are subjected to further analysis and quantitative evaluation by assumed measures of their operational performance.

A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem

2020 ◽  
Vol 28 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Rodrigo Naranjo ◽  
Matilde Santos ◽  
Luis Garmendia
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Number ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Trading System ◽  
Weighted Distance ◽  
Pattern Recognition Problem ◽  
Euclidean Distance Measure ◽  
Desirable Behavior

A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance between two FSNs is larger when they are in different GTFNs, and smaller when they are in the same GTFN. The advantage of the new measure is demonstrated on a fuzzy forecasting trading system over two different real stock markets, which provides better predictions with larger profits than those obtained using the Euclidean distance measure for the same system.

Learning Graph Matching Substitution Weights Based on the Ground Truth Node Correspondence

2016 ◽  
Vol 30 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Xavier Cortés ◽  
Francesc Serratosa
Keyword(s):  
Pattern Recognition ◽  
Distance Measure ◽  
Graph Matching ◽  
Ground Truth ◽  
Local Characteristic ◽  
Attributed Graphs ◽  
Structured Objects

In pattern recognition, it is usual to compare structured objects through attributed graphs in which nodes represent local parts and edges relations between them. Thus, each characteristic in the local parts is represented by different attributes on the nodes or edges. In this framework, the comparison between structured objects is performed through a distance between attributed graphs. If we want to correctly tune the distance and node correspondence between graphs, we need to add some weights on the node and edge attributes to gauge the importance of each local characteristic while defining the distance measure between graphs. In this paper, we present a method to learn the weights of each node and edge attribute such that the distance between the ground truth correspondence between graphs and the automatically obtained correspondence is minimized.

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