Classification Trees as Proxies

2015 ◽  
Vol 2 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Anthony Scime ◽  
Nilay Saiya ◽  
Gregg R. Murray ◽  
Steven J. Jurek
Keyword(s):  
Data Analysis ◽  
Classification Trees ◽  
Original Model ◽  
Classification Model ◽  
Model Based ◽  
Alternative Characterization

In data analysis, when data are unattainable, it is common to select a closely related attribute as a proxy. But sometimes substitution of one attribute for another is not sufficient to satisfy the needs of the analysis. In these cases, a classification model based on one dataset can be investigated as a possible proxy for another closely related domain's dataset. If the model's structure is sufficient to classify data from the related domain, the model can be used as a proxy tree. Such a proxy tree also provides an alternative characterization of the related domain. Just as important, if the original model does not successfully classify the related domain data the domains are not as closely related as believed. This paper presents a methodology for evaluating datasets as proxies along with three cases that demonstrate the methodology and the three types of results.

scholarly journals Application of Innovative TGA/Chemometric Approach for Forensic Purposes: The Estimation of the Time since Death in Contaminated Specimens

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Roberta Risoluti ◽  
Giuseppina Gullifa ◽  
Vittorio Fineschi ◽  
Paola Frati ◽  
Stefano Materazzi
Keyword(s):  
Multidisciplinary Approach ◽  
Vitreous Humor ◽  
Classification Model ◽  
Forensic Sciences ◽  
Time Since Death ◽  
Time Of Death ◽  
Novel Approach ◽  
Chemometric Approach ◽  
Postmortem Examinations

Chronothanatology has always been a challenge in forensic sciences. Therefore, the importance of a multidisciplinary approach for the characterization of matrices (organs, tissues, or fluids) that respond linearly to the postmortem interval (PMI) is emerging increasingly. The vitreous humor is particularly suitable for studies aimed at assessing time-related modifications because it is topographically isolated and well-protected. In this work, a novel approach based on thermogravimetry and chemometrics was used to estimate the time since death in the vitreous humor and to collect a databank of samples derived from postmortem examinations after medico–legal evaluation. In this study, contaminated and uncontaminated specimens with tissue fragments were included in order to develop a classification model to predict time of death based on partial least squares discriminant analysis (PLS-DA) that was as robust as possible. Results demonstrate the possibility to correctly predict the PMI even in contaminated samples, with an accuracy not lower than 70%. In addition, the correlation coefficient of the measured versus predicted outcomes was found to be 0.9978, confirming the ability of the model to extend its feasibility even to such situations involving contaminated vitreous humor.

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

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