Data mining in manufacturing: Significance analysis of process parameters

2008 ◽  
Vol 222 (11) ◽  
pp. 1503-1516 ◽  
Author(s):  
M Perzyk ◽  
R Biernacki ◽  
J Kozlowski
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Process Parameters ◽  
Regression Models ◽  
Simulated Data ◽  
Real Data ◽  
Support Vector ◽  
Data Sets ◽  
Interaction Factors

Determination of the most significant manufacturing process parameters using collected past data can be very helpful in solving important industrial problems, such as the detection of root causes of deteriorating product quality, the selection of the most efficient parameters to control the process, and the prediction of breakdowns of machines, equipment, etc. A methodology of determination of relative significances of process variables and possible interactions between them, based on interrogations of generalized regression models, is proposed and tested. The performance of several types of data mining tool, such as artificial neural networks, support vector machines, regression trees, classification trees, and a naïve Bayesian classifier, is compared. Also, some simple non-parametric statistical methods, based on an analysis of variance (ANOVA) and contingency tables, are evaluated for comparison purposes. The tests were performed using simulated data sets, with assumed hidden relationships, as well as on real data collected in the foundry industry. It was found that the performance of significance and interaction factors obtained from regression models, and, in particular, neural networks, is satisfactory, while the other methods appeared to be less accurate and/or less reliable.

scholarly journals Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings

2021 ◽  
Vol 13 (5) ◽  
pp. 2426
Author(s):  
David Bienvenido-Huertas ◽  
Jesús A. Pulido-Arcas ◽  
Carlos Rubio-Bellido ◽  
Alexis Pérez-Fargallo
Keyword(s):  
Low Income ◽  
Potential Risk ◽  
Energy Use ◽  
Risk Index ◽  
Computing Time ◽  
Simulated Data ◽  
Real Data ◽  
Support Vector ◽  
Energy Poverty ◽  
Regression Algorithms

In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.

An Unsupervised Approach for Determining Link Specifications

2018 ◽  
Vol 13 (4) ◽  
pp. 104-123
Author(s):  
Khayra Bencherif ◽  
Mimoun Malki ◽  
Djamel Amar Bensaber
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Real Data ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sets ◽  
Novel Approach ◽  
Link Discovery ◽  
Unsupervised Approach

This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

scholarly journals Neural Networks for the Joint Development of Individual Payments and Claim Incurred

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 33
Author(s):  
Łukasz Delong ◽  
Mario V. Wüthrich
Keyword(s):  
Neural Networks ◽  
Regression Models ◽  
Development Process ◽  
Real Data ◽  
Data Set ◽  
Joint Development ◽  
Chain Ladder Method ◽  
History Of ◽  
Feature Information ◽  
Chain Ladder

The goal of this paper is to develop regression models and postulate distributions which can be used in practice to describe the joint development process of individual claim payments and claim incurred. We apply neural networks to estimate our regression models. As regressors we use the whole claim history of incremental payments and claim incurred, as well as any relevant feature information which is available to describe individual claims and their development characteristics. Our models are calibrated and tested on a real data set, and the results are benchmarked with the Chain-Ladder method. Our analysis focuses on the development of the so-called Reported But Not Settled (RBNS) claims. We show benefits of using deep neural network and the whole claim history in our prediction problem.

A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomes

2017 ◽  
Vol 28 (4) ◽  
pp. 1105-1125 ◽  
Author(s):  
Chuen Seng Tan ◽  
Nathalie C Støer ◽  
Ying Chen ◽  
Marielle Andersson ◽  
Yilin Ning ◽  
...  
Keyword(s):  
Regression Models ◽  
Proportional Hazards ◽  
Menopausal Status ◽  
Marketing Research ◽  
Simulated Data ◽  
Real Data ◽  
Tertiary Hospital ◽  
Research Literature ◽  
Cox Proportional Hazards ◽  
Continuous Outcomes

The control of confounding is an area of extensive epidemiological research, especially in the field of causal inference for observational studies. Matched cohort and case-control study designs are commonly implemented to control for confounding effects without specifying the functional form of the relationship between the outcome and confounders. This paper extends the commonly used regression models in matched designs for binary and survival outcomes (i.e. conditional logistic and stratified Cox proportional hazards) to studies of continuous outcomes through a novel interpretation and application of logit-based regression models from the econometrics and marketing research literature. We compare the performance of the maximum likelihood estimators using simulated data and propose a heuristic argument for obtaining the residuals for model diagnostics. We illustrate our proposed approach with two real data applications. Our simulation studies demonstrate that our stratification approach is robust to model misspecification and that the distribution of the estimated residuals provides a useful diagnostic when the strata are of moderate size. In our applications to real data, we demonstrate that parity and menopausal status are associated with percent mammographic density, and that the mean level and variability of inpatient blood glucose readings vary between medical and surgical wards within a national tertiary hospital. Our work highlights how the same class of regression models, available in most statistical software, can be used to adjust for confounding in the study of binary, time-to-event and continuous outcomes.

A Growth Model for Multilevel Ordinal Data

2005 ◽  
Vol 30 (4) ◽  
pp. 369-396 ◽  
Author(s):  
Eisuke Segawa
Keyword(s):  
Latent Variable ◽  
Ordinal Data ◽  
Linear Models ◽  
Growth Models ◽  
Simulated Data ◽  
Real Data ◽  
Analytic Structure ◽  
Data Sets ◽  
Data Set ◽  
Time Points

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can analyze not only data with item- and time-level missing observations, but also data with time points freely specified over subjects. Furthermore, features useful for longitudinal analyses, “autoregressive error degree one” structure for the trait residuals and estimated time-scores, were included. The approach is Bayesian with Markov Chain and Monte Carlo, and the model is implemented in WinBUGS. They are illustrated with two simulated data sets and one real data set with planned missing items within a scale.

PERFORMANCE EVALUATION OF THREE PATTERN CLASSIFICATION TECHNIQUES USED FOR WATER QUALITY MONITORING

2012 ◽  
Vol 11 (02) ◽  
pp. 1250013 ◽  
Author(s):  
M. BOUAMAR ◽  
M. LADJAL
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Human Life ◽  
Recognition Rate ◽  
Real Data ◽  
Water Quality Monitoring ◽  
Support Vector ◽  
Rbf Neural Networks ◽  
Training Time ◽  
Classification Tool

Water quality is one of the major concerns of countries around the world. Monitoring of water quality is becoming more and more interesting because of its effects on human life. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Many techniques were developed in order to improve this process attending to rigorous follow-ups of the water quality. In this paper, we present a comparative study of the performance of three techniques resulting from the field of the artificial intelligence namely: Artificial Neural Networks (ANN), RBF Neural Networks (RBF-NN), and Support Vector Machines (SVM). Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. In order to evaluate their performances regarding the recognition rate, training time, and robustness, a simulation using generated and real data is carried out. To validate their functionalities, an application performed on real data is presented. Applied as a classification tool, the technique selected should ensure, within a multisensor monitoring system, a direct and quasi permanent control of water quality.

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