8780756
A Modular k-Nearest Neighbor Classification Method for Massively Parallel Text Categorization
This paper presents a Min-Max modular k-nearest neighbor (M3-k-NN) classification method for massively parallel text categorization. The basic idea behind the method is to decompose a large-scale text categorization problem into a number of smaller two-class subproblems and combine all of the individual modular k-NN classifiers trained on the smaller two-class subproblems into an M3-k-NN classifier. Our experiments in text categorization demonstrate that M3-k-NN is much faster than conventional k-NN, and meanwhile the classification accuracy of M3-k-NN is slightly better than that of the conventional k-NN. In practical, M3-k-NN has intimate relationship with high order k-NN algorithm; therefore, in theoretical sense, the reliability of M3-k-NN has been supported to some extend.
15074010
A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning
A difficulty faced by existing reduction techniques for k-NN algorithm is to require loading the whole training data set. Therefore, these approaches often become inefficient when they are used for solving large-scale problems. To overcome this deficiency, we propose a new method for reducing samples for k-NN algorithm. The basic idea behind the proposed method is a self-recombination learning strategy, which is originally designed for combining classifiers to speed up response time by reducing the number of base classifiers to be checked and improve the generalization performance by rearranging the order of training samples. Experimental results on several benchmark problems indicate that the proposed method is valid and efficient.
16473944
Analysis of Fault Tolerance of a Combining Classifier
This paper mainly analyses the fault tolerant capability of a combining classifier that uses a K-voting strategy for integrating binary classifiers. From the point view of fault tolerance, we discuss the influence of the failure of binary classifiers on the final output of the combining classifier, and present a theoretical analysis of combination performance under three fault models. The results provide a theoretical base for fault detection of the combining classifier.
17057402
"I hear the pattern": interactive sonification of geographical data patterns
Interactive sonification (non-speech sound) is a novel strategy to present the geographical distribution patterns of statistical data to vision impaired users. We discuss the design space with dimensions of interaction actions, data representation forms, input devices, navigation structures, and sound feedback encoding. Two interfaces were designed, one using a keyboard and another using a smooth surface touch tablet. A study with three blind users shows that they are able to perceive patterns of 5-category values on both familiar and unknown maps, and learn new map geography, in both interfaces.
21716413
Advance Reservation Policies for Workflows
Advance reservation of resources has been suggested as a means to provide a certain level of support that meets user expectations with respect to specific job start times in parallel systems. Those expectations may relate to a single job application or an application that consists of a collection of dependent jobs. In the context of Grid computing, applications consisting of dependent tasks become increasingly important, usually known as workflows. This paper focuses on the problem of planning advance reservations for individual tasks of workflow-type of applications when the user specifies a requirement only for the whole workflow application. Two policies to automate advance reservation planning for individual tasks efficiently are presented and evaluated.
18920027
A computational approach for ordering signal transduction pathway components from genomics and proteomics Data
BackgroundSignal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.ResultsWe have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.ConclusionsAs demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms.
1436574
Scheduling multiple DAGs onto heterogeneous systems
The problem of scheduling a single DAG onto heterogeneous systems has been studied extensively. In this paper, we focus on the problem of scheduling more than one DAG at the same time onto a set of heterogeneous resources. The aim is not only to optimize the overall makespan, but also to achieve fairness, defined on the basis of the slowdown that each DAG would experience as a result of competing for resources with other DAGs. Two policies particularly focussing to deliver fairness are presented and evaluated along with another four policies that can be used to schedule multiple DAGs.
2217384
A semiparametric approach for marker gene selection based on gene expression data
MOTIVATION
Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification.


RESULTS
A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates.


CONTACT
hongyu.zhao@yale.edu.