A Hybrid KNN algorithm with Sugeno measure for the personal credit reference system in China

2020 ◽  
Vol 39 (5) ◽  
pp. 6993-7004
Author(s):  
Lu Han ◽  
Zhi Su ◽  
Jing Lin
Keyword(s):  
Reference System ◽  
Euclidean Distance ◽  
Real Data ◽  
Distance Measures ◽  
Ordinal Variables ◽  
Personal Credit ◽  
Kmeans Algorithm ◽  
Sugeno Measure ◽  
Credit Data

Ever increasing ordinal variables are being collected by the Personal Credit Reference System in China, however this system suffers from analysis of this kind of data, which cannot be calculated by Euclidean distance. In this study, we put forward a hybrid KNN algorithm based on Sugeno measure, and we prove that the error of this algorithm is smaller than that of Euclidean distance, furthermore, we use real data obtained from the Personal Credit Reference System to perform experiments and get the user’s initial portrait. Through the comparisons with Kmeans algorithm and other different distance measures in KNN algorithm, we find that the hybrid KNN algorithm is more suitable for clustering personal credit data.

Linkage analysis using geographical proximity: a test of the efficacy of distance measures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shumpei Haginoya ◽  
Aiko Hanayama ◽  
Tamae Koike
Keyword(s):  
Environmental Factors ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Geographical Proximity ◽  
Discrimination Accuracy ◽  
Content Type ◽  
Shortest Route ◽  
The Impact

Purpose The purpose of this paper was to compare the accuracy of linking crimes using geographical proximity between three distance measures: Euclidean (distance measured by the length of a straight line between two locations), Manhattan (distance obtained by summing north-south distance and east-west distance) and the shortest route distances. Design/methodology/approach A total of 194 cases committed by 97 serial residential burglars in Aomori Prefecture in Japan between 2004 and 2015 were used in the present study. The Mann–Whitney U test was used to compare linked (two offenses committed by the same offender) and unlinked (two offenses committed by different offenders) pairs for each distance measure. Discrimination accuracy between linked and unlinked crime pairs was evaluated using area under the receiver operating characteristic curve (AUC). Findings The Mann–Whitney U test showed that the distances of the linked pairs were significantly shorter than those of the unlinked pairs for all distance measures. Comparison of the AUCs showed that the shortest route distance achieved significantly higher accuracy compared with the Euclidean distance, whereas there was no significant difference between the Euclidean and the Manhattan distance or between the Manhattan and the shortest route distance. These findings give partial support to the idea that distance measures taking the impact of environmental factors into consideration might be able to identify a crime series more accurately than Euclidean distances. Research limitations/implications Although the results suggested a difference between the Euclidean and the shortest route distance, it was small, and all distance measures resulted in outstanding AUC values, probably because of the ceiling effects. Further investigation that makes the same comparison in a narrower area is needed to avoid this potential inflation of discrimination accuracy. Practical implications The shortest route distance might contribute to improving the accuracy of crime linkage based on geographical proximity. However, further investigation is needed to recommend using the shortest route distance in practice. Given that the targeted area in the present study was relatively large, the findings may contribute especially to improve the accuracy of proactive comparative case analysis for estimating the whole picture of the distribution of serial crimes in the region by selecting more effective distance measure. Social implications Implications to improve the accuracy in linking crimes may contribute to assisting crime investigations and the earlier arrest of offenders. Originality/value The results of the present study provide an initial indication of the efficacy of using distance measures taking environmental factors into account.

scholarly journals Some Approaches to the Rolling Wheels’ Dynamics Modelling in the Weight-in-Motion Problem

2013 ◽  
Vol 14 (1) ◽  
pp. 57-78
Author(s):  
Alexander Grakovski ◽  
Yuri Krasnitski ◽  
Igor Kabashkin ◽  
Victor Truhachov
Keyword(s):  
Reference System ◽  
Field Experiments ◽  
Finite Impulse Response ◽  
Vertical Component ◽  
Real Data ◽  
Point Sources ◽  
External Factors ◽  
Problem Solution ◽  
Dynamic Errors ◽  
The Impact

Abstract Some possibilities of fibre-optic sensors (FOS) application for measuring the weight of moving vehicles realized in weightin- motion (WIM) systems are discussed. As the first, the model of small-buried seismic sensor transient response excited by a car tyre interaction with asphalt-concrete road pavement is proposed. It is supposed that a seismic wave received by the sensor is the vertical component of surface Raleigh wave. The model is based on supposition that a tyre footprint is acceptable to consider as some array of point sources of these waves. The proper algorithms permit to vary different parameters of the array excitation, as to footprint dimensions, load distribution, car velocities and others. The set of Matlab codes is worked out for seismic pulses modelling and processing. The second way considered is to simulate the FOS signal in the basis of differential equations describing a deformable wheel behaviour, or wheel oscillations, in order to identify relations with optoelectronic mechanical parameters. An attempt to find the mass of the vehicle is based on minimizing the discrepancy between the actual FOS signal and the solution of the differential equation. The accuracy of the evaluated weight depends on many external factors, the mathematical modelling of them are expressed in the numerical values of the coefficients and external stimuli. The influence of these factors are analysed and tested by simulations and field experiments. One of ideas in dynamic weighing problem solution should consist in evaluation of position of virtual gravity centre of the vehicle in time. The processing algorithm of the data received from the FOS is proposed based on conception of database retaining in some reference system memory. Certain requirements concerning the elements and blocks of the algorithm are defined as well. The reference system is realized as the digital filter with the finite impulse response. The method to estimate the filter coefficients is worked out. Several experiments with this algorithm have been carried out for the vehicle identification with the reference loads adopted from real data. The different factors have an influence on the measurement accuracy of FOS. The roadbed features, temperature, nonlinearities and delay effects in FOS are among them. The results of laboratory and field measurements with FOS responses to different axle’s loadings are presented. Charging and inertial characteristics of FOS under the impact of various external factors (protective cover, temperature, contact area, and installation mode especially) as well as their approximations are investigated. It is found that the final calibration of the FOS has to be done individually and only after it has been installed in the pavement. Certain methods and algorithms of linearization, as well temperature and dynamic errors compensation of FOS data are discussed.

A Finding Method of Shape Theme Based on Wavelet Time Series

2013 ◽  
Vol 756-759 ◽  
pp. 3199-3203
Author(s):  
Qiu Guo ◽  
Lu Guo
Keyword(s):  
Euclidean Distance ◽  
Search Algorithm ◽  
Real Data ◽  
Data Sets ◽  
Real Time Processing ◽  
The Real ◽  
Multi Resolution Analysis ◽  
Processing Ability ◽  
Resolution Analysis ◽  
Finding Method

Finding shape theme has raised great attention in the database of shapes. According to the problem of incompatible about accuracy and complexity in the shape theme search algorithm ,this paper proposed a finding theme algorithm using the multi-resolution analysis of wavelet and the processing capability of reduction dimension of time sequence , accurately calculated the similarity between different objects combining with the Euclidean distance formula, and achieved satisfactory results. Through the comparison between the real data sets to test and traditional shape theme algorithm, it shows that the method has good stability and reliability, and ensure the real-time processing ability of the closed contour shapes overall matching.

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.

scholarly journals Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

2016 ◽  
Vol 8 (2) ◽  
pp. 23
Author(s):  
Songul Cinaroglu
Keyword(s):  
Euclidean Distance ◽  
Time Series Data ◽  
Distance Measure ◽  
Health Expenditures ◽  
Oecd Countries ◽  
Distance Measures ◽  
Series Data ◽  
Longest Common Subsequences ◽  
Study Results ◽  
Euclidean Distance Measure

<p>Out of pocket health expenditures points out to the payments made by households at the point<br />they receive health services. Frequently these include doctor consultation fees, purchase of<br />medication and hospital bills. In this study hierarchical clustering method was used for<br />classification of 34 countries which are members of OECD (Organization for Economic<br />Cooperation and Development) in terms of out of pocket health expenditures for the years<br />between 1995-2011. Longest common subsequences (LCS), correlation coefficient and<br />Euclidean distance measure was used as a measure of similarity and distance in hierarchical<br />clustering. At the end of the analysis it was found that LCS and Euclidean distance measures<br />were the best for determining clusters. Furthermore, study results led to understand grouping<br />of OECD countries according to health expenditures.</p>

Least Squares Method for Attitude Determination Using the Real Data of CBERS-2 Satellite

2014 ◽  
Vol 706 ◽  
pp. 181-190 ◽  
Author(s):  
W.R. Silva ◽  
H.K. Kuga ◽  
M.C. Zanardi ◽  
R.V. Garcia
Keyword(s):  
Reference System ◽  
Attitude Determination ◽  
Least Squares Method ◽  
Real Data ◽  
The Body ◽  
Space Environment ◽  
Artificial Satellites ◽  
Body Frame ◽  
Control Center ◽  
Yaw Attitude

his work is applied to the dynamics of rotational motion of artificial satellites, that is, itsorientation (attitude) with respect to an inertial reference system. The attitude determination involvesapproaches of nonlinear estimation techniques, which knowledge is essential to the safety and controlof the satellite and payload. Here one focuses on determining the attitude of a real satellite: CBERS-2(China Brazil Earth Resources Satellite). This satellite was launched in 2003 and were controlled andoperated in turns by China (Xi’an Control Center) and Brazil (Satellite Control Center). Its orbit isnear polar sun-synchronous with an altitude of 778km, crossing Equator at 10:30am in descendingdirection, frozen perigee at 90 degrees, and providing global coverage of the world every 26 days.The attitude dynamical model is described by nonlinear equations involving the Euler angles. Theattitude sensors available are two DSS (Digital Sun Sensor), two IRES (Infra-Red Earth Sensor), andone triad of mechanical gyros. The two IRES give direct measurements of roll and pitch angles with acertain level of error. The two DSS are nonlinear functions of roll, pitch, and yaw attitude angles. Thegyros furnish the angular measurements in the body frame reference system. Gyros are very importantsensors, as they provide direct incremental angles or angular velocities. They can sense instantaneousvariations of nominal velocities. An important feature is that it allows the replacement of complexmodels (different torques acting on the space environment) by using their measurements to turn thedynamical equations into simple kinematic equations. However gyros present several sources of errorof which the drift is the most troublesome. Such drifts yield along time an accumulation of errorswhich must be accounted for in the attitude determination process. Herein one proposes to estimatethe attitude and the drift of the gyros using the Least SquaresMethod. Results show that one can reachaccuracies in attitude determination within the prescribed requirements, besides providing estimatesof the gyro drifts which can be further used to enhance the gyro error model.

scholarly journals LA PROTECCIÓN DE LOS DATOS PERSONALES EN EL SISTEMA DE REPORTE DE CRÉDITOS PERUANO

2019 ◽  
pp. 73-94
Author(s):  
RAÚL VÁSQUEZ RODRÍGUEZ
Keyword(s):  
Data Protection ◽  
Personal Data ◽  
Constitutional Court ◽  
Personal Credit ◽  
Personal Data Protection ◽  
Credit Risks ◽  
El Sistema ◽  
Private Center ◽  
The Law ◽  
Credit Data

Este trabajo analiza la interacción entre el tratamiento de información crediticia de las personas naturales a cargo de las centrales de riesgo, debido a su importancia para los agentes de mercado, y el derecho fundamental a la protección de los datos personales, profundizando en el trasfondo constitucional de los derechos involucrados, en las interpretaciones del Tribunal Constitucional y en la compatibilidad de objetivos respecto de la privacidad y la confidencialidad que existe entre la Ley N.° 27489, Ley que regula las Centrales Privadas de Información de Riesgos y de Protección al Titular de la Información y la Ley N.° 29733, Ley de Protección de Datos Personales y su reglamento; interacción analizada por la Autoridad Nacional de Protección de Datos Personales en sus resoluciones directorales. This work researchs the interaction between the personal credit data processing of natural people in charge of the Credit Risks Agencies, because of its relevance for stakeholders and the fundamental right for the personal data protection, deepening in the constitutional background of the involved rights, in the Constitutional Court statements and the compatibility whith the privacy and confidentiality targets that exists between the Law N.° 27489, which regulates the Credit Risk Information Private Center and the Data Holder Protection Act, and the Law N.° 29733, law for the Personal Data Protection and its rulement; which is analyzed by Personal Data Protection Agency in its directoral judgments.

scholarly journals Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 753
Author(s):  
Wenyuan Zhang ◽  
Xijuan Guo ◽  
Tianyu Huang ◽  
Jiale Liu ◽  
Jun Chen
Keyword(s):  
Bias Correction ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Similarity Measures ◽  
Distance Measures ◽  
Background Information ◽  
Local Similarity ◽  
Original Algorithm ◽  
Fcm Clustering

The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.

scholarly journals An innovative picture fuzzy distance measure and novel multi-attribute decision-making method

2021 ◽  
Author(s):  
Abdul Haseeb Ganie ◽  
Surender Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Real Data ◽  
Distance Measures ◽  
Classification Problems ◽  
Data Set ◽  
The Real ◽  
Picture Fuzzy Set ◽  
Multi Attribute Decision Making

AbstractPicture fuzzy set (PFS) is a direct generalization of the fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). The concept of PFS is suitable to model the situations that involve more answers of the type yes, no, abstain, and refuse. In this study, we introduce a novel picture fuzzy (PF) distance measure on the basis of direct operation on the functions of membership, non-membership, neutrality, refusal, and the upper bound of the function of membership of two PFSs. We contrast the proposed PF distance measure with the existing PF distance measures and discuss the advantages in the pattern classification problems. The application of fuzzy and non-standard fuzzy models in the real data is very challenging as real data is always found in crisp form. Here, we also derive some conversion formulae to apply proposed method in the real data set. Moreover, we introduce a new multi-attribute decision-making (MADM) method using the proposed PF distance measure. In addition, we justify necessity of the newly proposed MADM method using appropriate counterintuitive examples. Finally, we contrast the performance of the proposed MADM method with the classical MADM methods in the PF environment.

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