15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
12928468
Distributed goal recognition algorithms for modular robots
Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.
10936975
Quantifying Evolving Processes in Multimodal 3D Medical Images
Quantitative measurements of changes in evolving brain pathology, such as multiple sclerosis lesions and brain tumors, are important for clinicians to perform pertinent diagnoses and to help in patient follow-up. Lesions or tumors can vary over time in size, shape, location and composition because of natural pathological processes or the effect of a drug treatment or therapy. In the past, people have used as a quantitative measurement the change in total or regional lesion/tumor volume. In this paper we propose a new model to quantify changes in evolving processes in multimodal 3D medical images. We believe this model reflects changes in pathology more accurately because it simultaneously takes into account information in multiple imaging modalities and the location of lesion/tumor voxels. We demonstrate the effectiveness of this model with experiments on synthetic lesion data.
15617422
R*-Histograms: efficient representation of spatial relations between objects of arbitrary topology
Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N<sup>2</sup>) time algorithm for the R-Histogram even when k=O(n). Here, N=n<sup>2</sup> denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.
3069530
HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
MOTIVATION
Recent studies have shown that microarray gene expression data are useful for phenotype classification of many diseases. A major problem in this classification is that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. Many approaches have been proposed for this gene selection problem. Most of the previous gene ranking methods typically select 50-200 top-ranked genes and these genes are often highly correlated. Our goal is to select a small set of non-redundant marker genes that are most relevant for the classification task.


RESULTS
To achieve this goal, we developed a novel hybrid approach that combines gene ranking and clustering analysis. In this approach, we first applied feature filtering algorithms to select a set of top-ranked genes, and then applied hierarchical clustering on these genes to generate a dendrogram. Finally, the dendrogram was analyzed by a sweep-line algorithm and marker genes are selected by collapsing dense clusters. Empirical study using three public datasets shows that our approach is capable of selecting relatively few marker genes while offering the same or better leave-one-out cross-validation accuracy compared with approaches that use top-ranked genes directly for classification.


AVAILABILITY
The HykGene software is freely available at http://www.cs.dartmouth.edu/~wyh/software.htm


CONTACT
wyh@cs.dartmouth.edu


SUPPLEMENTARY INFORMATION
Supplementary material is available from http://www.cs.dartmouth.edu/~wyh/hykgene/supplement/index.htm.
53224637
Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram
15418757
Fingerprint classification by directional fields
Fingerprint classification provides an important fingerprint index and can reduce fingerprint matching time in a large database. A good classification algorithm can give an accurate index that is able to search a fingerprint database more effectively. We present a fingerprint classification algorithm that is based on directional fields. We compute directional fields of fingerprint images and detect singular points (cores). Then, we extract features that we define from fingerprint images. We also use k-means classifier and 3-nearest neighbor to classify features and distinguish which fingerprint is Arch, Left Loop, Right Loop, or Whorl. Experimental results show a significant improvement in fingerprint classification performance. Moreover, the time required for the classification algorithm is reduced.
12789585
A Secure Agent Architecture for Sensor Networks
Advances in sensor and wireless technologies will enable thousands, or even millions of small but smart sensors to be deployed for a wide range of monitoring applications. The sensor networks must be dynamically configurable and adaptive in order to response actively to events. Security must also be built into sensor networks at the very initial design stage to prevent any potential threats. This paper presents a design of a generic software architecture based on tiny active agents for sensor networks. The architecture has built-in security components. This architecture will significantly benefit the dynamic and secure deployment and configuration tasks of large sensor networks for various applications.
6612123
Automatic Misconfiguration Troubleshooting with PeerPressure
Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. 
 
In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. 
 
In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.
2472095
Coarse and Reliable Geometric Alignment for Protein Docking
We present an efficient algorithm for generating a small set of coarse alignments between interacting proteins using meaningful features on their surfaces. The proteins are treated as rigid bodies, but the results are more generally useful as the produced configurations can serve as input to local improvement algorithms that allow for protein flexibility. We apply our algorithm to a diverse set of protein complexes from the Protein Data Bank, demonstrating the effectivity of our algorithm, both for bound and for unbound protein docking problems.
47345540
Shape Fitting with Outliers
Given a set $\mathcal{H}$ of n hyperplanes in ${\Bbb R}^d$, we present an algorithm that $\eps$-approximates the extent between the top and bottom k levels of the arrangement of $\mathcal{H}$ in time $O(n + (k/\eps)^c)$, where c is a constant depending on d. The algorithm relies on computing a subset of $\mathcal{H}$ of size $O(k/\eps^{d-1})$, in near linear time, such that the k-level of the arrangement of the subset approximates that of the original arrangement. Using this algorithm, we propose efficient approximation algorithms for shape fitting with outliers for various shapes. These are the first algorithms to handle outliers efficiently for the shape fitting problems considered.