227,295 research outputs found
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm
The identification of different homogeneous groups of observations and their
appropriate analysis in PLS-SEM has become a critical issue in many appli-
cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of all
units on which the model is estimated, and approaches of segmentation present
in literature, consist in estimating separate models for each segments of
statistical units, which have been obtained either by assigning the units to
segments a priori defined. However, these approaches are not fully accept- able
because no causal structure among the variables is postulated. In other words,
a modeling approach should be used, where the obtained clusters are homogeneous
with respect to the structural causal relationships. In this paper, a new
methodology for simultaneous non-hierarchical clus- tering and PLS-SEM is
proposed. This methodology is motivated by the fact that the sequential
approach of applying first SEM or PLS-SEM and second the clustering algorithm
such as K-means on the latent scores of the SEM/PLS-SEM may fail to find the
correct clustering structure existing in the data. A simulation study and an
application on real data are included to evaluate the performance of the
proposed methodology
Reliability approach in spacecraft structures
This paper presents an application of the probabilistic approach with reliability assessment on a spacecraft structure. The adopted strategy uses meta-modeling with first and second order polynomial functions. This method aims at minimizing computational time while giving relevant results. The first part focuses on computational tools employed in the strategy development. The second part presents a spacecraft application. The purpose is to highlight benefits of the probabilistic approach compared with the current deterministic one. From examples of reliability assessment we show some advantages which could be found in industrial applications
Sample- and segment-size specific Model Selection in Mixture Regression Analysis
As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or product positioning.
This paper presents the results of an extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context with normal data. Unlike with previous studies, the performance is evaluated at a broad range of sample/segment size combinations being the most critical factors for the effectiveness of the criteria from both a theoretical and practical point of view. In order to assess the absolute performance of each criterion with respect to chance, the performance is reviewed against so called chance criteria, derived from discriminant analysis.
The results induce recommendations on criterion selection when a certain sample size is given and help to judge what sample size is needed in order to guarantee an accurate decision based on a certain criterion respectively
Ensemble of Example-Dependent Cost-Sensitive Decision Trees
Several real-world classification problems are example-dependent
cost-sensitive in nature, where the costs due to misclassification vary between
examples and not only within classes. However, standard classification methods
do not take these costs into account, and assume a constant cost of
misclassification errors. In previous works, some methods that take into
account the financial costs into the training of different algorithms have been
proposed, with the example-dependent cost-sensitive decision tree algorithm
being the one that gives the highest savings. In this paper we propose a new
framework of ensembles of example-dependent cost-sensitive decision-trees. The
framework consists in creating different example-dependent cost-sensitive
decision trees on random subsamples of the training set, and then combining
them using three different combination approaches. Moreover, we propose two new
cost-sensitive combination approaches; cost-sensitive weighted voting and
cost-sensitive stacking, the latter being based on the cost-sensitive logistic
regression method. Finally, using five different databases, from four
real-world applications: credit card fraud detection, churn modeling, credit
scoring and direct marketing, we evaluate the proposed method against
state-of-the-art example-dependent cost-sensitive techniques, namely,
cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision
trees. The results show that the proposed algorithms have better results for
all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio
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