9259468
Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data
MOTIVATION
High-density DNA microarray measures the activities of several thousand genes simultaneously and the gene expression profiles have been used for the cancer classification recently. This new approach promises to give better therapeutic measurements to cancer patients by diagnosing cancer types with improved accuracy. The Support Vector Machine (SVM) is one of the classification methods successfully applied to the cancer diagnosis problems. However, its optimal extension to more than two classes was not obvious, which might impose limitations in its application to multiple tumor types. We briefly introduce the Multicategory SVM, which is a recently proposed extension of the binary SVM, and apply it to multiclass cancer diagnosis problems.


RESULTS
Its applicability is demonstrated on the leukemia data (Golub et al., 1999) and the small round blue cell tumors of childhood data (Khan et al., 2001). Comparable classification accuracy shown in the applications and its flexibility render the MSVM a viable alternative to other classification methods.


SUPPLEMENTARY INFORMATION
http://www.stat.ohio-state.edu/~yklee/msvm.htm
7659070
Statistical Properties and Adaptive Tuning of Support Vector Machines
In this paper we consider the statistical aspects of support vector machines (SVMs) in the classification context, and describe an approach to adaptively tuning the smoothing parameter(s) in the SVMs. The relation between the Bayes rule of classification and the SVMs is discussed, shedding light on why the SVMs work well. This relation also reveals that the misclassification rate of the SVMs is closely related to the generalized comparative Kullback-Leibler distance (GCKL) proposed in Wahba (1999, Scholkopf, Burges, & Smola (Eds.), Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press). The adaptive tuning is based on the generalized approximate cross validation (GACV), which is an easily computable proxy of the GCKL. The results are generalized to the unbalanced case where the fraction of members of the classes in the training set is different than that in the general population, and the costs of misclassification for the two kinds of errors are different. The main results in this paper have been obtained in several places elsewhere. Here we take the opportunity to organize them in one place and note how they fit together and reinforce one another. Mostly the work of the authors is reviewed.
5801524
Support Vector Machines for Classification in Nonstandard Situations
The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come from the same distribution as that of the target population, and that the cost of misclassification into different classes are the same. However, these assumptions are often violated in real world settings. For some classification methods, this can often be taken care of simply with a change of threshold; for others, additional effort is required. In this paper, we explain why the standard support vector machine is not suitable for the nonstandard situation, and introduce a simple procedure for adapting the support vector machine methodology to the nonstandard situation. Theoretical justification for the procedure is provided. Simulation study illustrates that the modified support vector machine significantly improves upon the standard support vector machine in the nonstandard situation. The computational load of the proposed procedure is the same as that of the standard support vector machine. The procedure reduces to the standard support vector machine in the standard situation.
45657020
Partial convexification cuts for 0-1 mixed-integer programs
Abstract In this research, we propose a new cut generation scheme based on constructing a partial convex hull representation for a given 0–1 mixed-integer programming problem by using the reformulation–linearization technique (RLT). We derive a separation problem that projects the extended space of the RLT formulation into the original space, in order to generate a cut that deletes a current fractional solution. Naturally, the success of such a partial convexification based cutting plane scheme depends on the process used to tradeoff the strength of the cut derived and the effort expended. Accordingly, we investigate several variable selection rules for performing this convexification, along with restricted versions of the accompanying separation problems, so as to be able to derive strong cuts within a reasonable effort. We also develop a strengthening procedure that enhances the generated cut by considering the binariness of the remaining unselected 0–1 variables. Finally, we present some promising computational results that provide insights into implementing the proposed cutting plane methodology.
9836575
Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach
Production-distribution planning is the most important activity in supply chain management (SCM). To solve this planning problem, either analytic or simulation approaches have been used. However these two approaches have their own demerits in problem solving. We propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation methods to solve production-distribution problems in supply chains. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and are adjusted by the proposed specific process according to the results from an independently developed simulation model which includes general production-distribution characteristics.
10094158
System and software designs for transaction processing in asymmetric communication environments
Observing that it is impractical to use traditional methods to control concurrency in broadcast-based asymmetric communication environments, we introduce a concurrency control protocol designed for broadcast-based transaction processing called Server Timestamp and Update Broadcast Supported Concurrency (STUBcast). STUBcast supports two new correctness criteria proposed in this paper—Single Serializability and Local Serializability. These criteria are weaker than global serializability, but are practical and easier to achieve in broadcast environments. This article also discusses the software design for the simulation program and the protocol implementation. Simulation results collected using our simulation program suggest that STUBcast could be very efficient in realistic application environments. Copyright © 2003 John Wiley & Sons, Ltd.
9799056
Fitting via alternative random-effect models
When there are two alternative random-effect models leading to the same marginal model, inferences from one model can be used for the other model. We illustrate how a likelihood method for fitting models with independent random effects can be applied to seemingly very different models with correlated random effects. We also discuss some merits of using these alternative models.
10552627
An efficient parallel join algorithm based on hypercube-partitioning
Many parallel join algorithms have been proposed so far but most of which are developed focused on minimizing the disk I/O and CPU costs. The communication cost, however, is also an important factor that can significantly affect the join processing performance in multiprocessor systems. We propose an efficient parallel join algorithm, called Cube-Robust, for hypercube multicomputers. The proposed algorithm is developed based on the observation that the size ratio of two relations to be joined is the dominant factor in the communication cost. We develop the analytic cost model for the proposed join algorithm. The performance comparisons show that the Cube-Robust join algorithm works better than others proposed earlier in a wide range of size ratios.<<ETX>>