1,200 research outputs found
Kernel Logistic Regression-linear for Leukemia Classification Using High Dimensional Data
Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernelâmachine techniques. Basely, KLR is kernelized version of linear Logistic Regression (LR). Unlike LR, KLR has ability to classify data with non linear boundary and also can accommodate data with very high dimensional and very few instances. In this research, we proposed to study the use of Linear Kernel on KLR in order to increase the accuracy of Leukemia Classification. Leukemia is one of the cancer types that causes mortality in medical diagnosis problem. Improving the accuracy of Leukemia Classification is essential for more effective diagnosis and treatment of Leukemia disease. The Leukemia data sets consists of 7120 (very high dimensional) DNA micro arrays data of 72 (very few instances) patient samples on the state of Leukemia types. In Leukemia classification based upon gene expression, monitoring data using DNA micro array offer hope to achieve an objective and highly accurate classification. It can be demonstrated that the use of Linear Kernel on Kernel Logistic Regression (KLRâLinear) can improve the performance in classifying Leukemia patient samples and also can be shown that KLRâLinear has better accuracy than KLRâPolynomial and Penalized Logistic Regression
Gradient-enhanced deep neural network approximations
We propose in this work the gradient-enhanced deep neural networks (DNNs)
approach for function approximations and uncertainty quantification. More
precisely, the proposed approach adopts both the function evaluations and the
associated gradient information to yield enhanced approximation accuracy. In
particular, the gradient information is included as a regularization term in
the gradient-enhanced DNNs approach, for which we present similar posterior
estimates (by the two-layer neural networks) as those in the path-norm
regularized DNNs approximations. We also discuss the application of this
approach to gradient-enhanced uncertainty quantification, and present several
numerical experiments to show that the proposed approach can outperform the
traditional DNNs approach in many cases of interests.Comment: 14 pages, 3 figure
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Aircraft industry is constantly striving for more efficient design
optimization methods in terms of human efforts, computation time, and resource
consumption. Hybrid surrogate optimization maintains high results quality while
providing rapid design assessments when both the surrogate model and the switch
mechanism for eventually transitioning to the HF model are calibrated properly.
Feedforward neural networks (FNNs) can capture highly nonlinear input-output
mappings, yielding efficient surrogates for aircraft performance factors.
However, FNNs often fail to generalize over the out-of-distribution (OOD)
samples, which hinders their adoption in critical aircraft design optimization.
Through SmOOD, our smoothness-based out-of-distribution detection approach, we
propose to codesign a model-dependent OOD indicator with the optimized FNN
surrogate, to produce a trustworthy surrogate model with selective but credible
predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits
inherent smoothness properties of the HF simulations to effectively expose OODs
through revealing their suspicious sensitivities, thereby avoiding
over-confident uncertainty estimates on OOD samples. By using SmOOD, only
high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading
to more accurate results at a low overhead cost. Three aircraft performance
models are investigated. Results show that FNN-based surrogates outperform
their Gaussian Process counterparts in terms of predictive performance.
Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases.
When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization
settings, they result in a decrease error rate of 34.65% and a computational
speed up rate of 58.36 times, respectively
Predicting Bankruptcy with Support Vector Machines
The purpose of this work is to introduce one of the most promising among recently developed statistical techniques â the support vector machine (SVM) â to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification
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