738592
A General Procedure for Combining Binary Classifiers and Its Performance Analysis
A general procedure for combining binary classifiers for multiclass classification problems with one-against-one decomposition policy is presented in this paper. Two existing schemes, namely the min-max combination and the most-winning combination, may be regarded as its two special cases. We show that the accuracy of the combination procedure will increase and time complexity will decrease as its main parameter increases under a proposed selection algorithm. The experiments verify our main results, and our theoretical analysis gives a valuable criterion for choosing different schemes of combining binary classifiers.
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.
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.
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.
7262387
An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm
This paper considers the Heterogeneous Earliest Finish Time (HEFT) algorithm for scheduling the tasks of an application, represented by a directed acyclic graph, onto a bounded number of heterogeneous machines. We focus on the appropriate selection of the weight for the nodes and edges of the graph, and experiment with a number of different schemes for computing these weights. Our findings indicate that the length of the schedule produced may be affected significantly by the scheme used, and suggest that the mean value based approach used by HEFT may not be a particularly good choice.
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.
1139392
Pathway analysis using random forests classification and regression
MOTIVATION
Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers.


RESULTS
In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data.


AVAILABILITY
Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.
7867544
A statistical method for identifying differential gene-gene co-expression patterns
MOTIVATION
To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states.


RESULTS
For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.
37263695
A complete solution to a conjecture on chromatic uniqueness of complete tripartite graphs
Let P(G,@l) be the chromatic polynomial of a graph G. A graph G is chromatically unique if for any graph H, P(H,@l)=P(G,@l) implies [email protected]?G. Koh, Teo and Chia conjectured that for any integers n and k with n>=k+2>=4, the complete tripartite graph K(n-k,n,n) is chromatically unique. Let K(n,m,r)-S denote the graph obtained by deleting all edges in S from the complete tripartite K(n,m,r). In this paper, we establish that for any positive integer n>=m>=r>=2, the chromatic equivalence class of K(n,m,r) is contained in the family {K(x,y,z)-S|1==2k and k>=2.