38177882
A Hybrid Feature Selection Approach for Microarray Gene Expression Data
Due to the huge number of genes and comparatively small number of samples from microarray gene expression data, accurate classification of diseases becomes challenging. Feature selection techniques can improve the classification accuracy by removing irrelevant and redundant genes. However, the performance of different feature selection algorithms based on different theoretic arguments varies even when they are applied to the same data set. In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm. The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.
9363924
RNA pseudoknot prediction using term rewriting
RNA plays a critical role in mediating every step of cellular information transfer from genes to functional proteins. Pseudoknots are widely occurring structural motifs found in all types of RNA and are also functionally important. Therefore predicting their structures is an important problem. In this paper, we present a new RNA pseudoknot prediction method based on term rewriting rather than on dynamic programming, comparative sequence analysis, or context-free grammars. The method we describe is implemented using the Mfold RNA/DNA folding package and the term rewriting language Maude. Our method was tested on 211 pseudoknots in PseudoBase and achieves an average accuracy of 74.085% compared to the experimentally determined structure. In fact, most pseudoknots discovered by our method achieve an accuracy of above 90%. These results indicate that term rewriting has a broad potential in RNA applications from prediction of pseudoknots to higher level RNA structures involving complex RNA tertiary interactions.
15500084
Understanding the Prediction of Transmembrane Proteins by Support Vector Machine using Association Rule Mining
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful
8422890
Spatiotemporal Phase Congruency Based Invariant Features for Human Behavior Classification
In applications of behavior recognition, the use of spatiotemporal invariant feature points can improve the robustness to noise, illumination and geometric distortions. In this paper, we develop a novel detection model of spatiotemporal invariant feature by generalizing the notion of image phase congruency to video volume phase congruency. The proposed model detects feature points by measuring the spatiotemporal phase congruency of Fourier series components along with their characteristic scale and principal orientation. Compared with other state-of-the-art methods, the key advantages of this interest point detector include the invariance to contrast variations and more precise feature location. Furthermore, an invariant feature descriptor is advanced based on the phase congruency map, resulting in enhanced discriminative power in classification tasks. Experimental results on KTH human motion dataset demonstrate the validity and effectiveness of the extracted invariant features in the human behavior recognition scheme.
4594972
New Fair Payment Protocols
Fair payment protocol is designed to guarantee fairness in electronic purchasing, that is, no party can falsely deny involvement in the transaction or having sent/received the specific items/payment. In this paper we first present an efficient fair payment protocol providing invisibility of TTP, timeliness, and standard RSA signatures as the final non-repudiation evidences. Then we present a second payment protocol which is the first one to provide abuse-freeness. Our protocols can be easily integrated into the existing electronic payment systems.
4379994
Interdomain Routing as Social Choice
Interdomain routing is essential to both the stability and efficiency of the global Internet. However, most previous studies focus only on stability, and only on a special class of routing protocols, namely BGP-type, path-vector protocols. In this paper, we conduct a systematic analysis of interdomain routing considering optimality and implementation in strategic settings. We adopt the novel perspective that an interdomain routing system is one which defines a social choice rule that aggregates individual preferences of all of the autonomous systems (ASes) in a network to select interdomain routes with a set of desirable properties. An interdomain routing protocol, then, is a mechanism to implement the identified interdomain routing social choice rule, when the ASes can adopt strategic actions. By pointing out the incompatibility among the desirable properties of an interdomain routing system and the requirements for strategic implementation in distributed settings, we reveal fundamental tradeoffs that must be made when extending BGP or designing the next-generation interdomain routing system. We also provide new insights into BGP, by "reverseengineering" its behaviors from the perspective of social choice and implementation theory.
44811386
Classification of Polarimetric SAR Data Based on Multidimensional Watershed Clustering
This paper proposes a polarimetric synthetic aperture radar (PolSAR) data classification method which applies multi-dimensional transform to identify density peaks and valleys for polarimetric signatures clustering. The new approach firstly introduces an improved maximum homogeneous region filter which can effectively preserve structure feature and polarimetric signatures. Then polarimetric signatures are extracted based on Freeman-Durden three-component composition. Finally, we obtain the classification results by multi-dimensional watershed clustering on the extracted polarimetric signatures. The effectiveness of this classification scheme is demonstrated using the full polarimetric L-band SAR imagery.
804068
Evaluation of RSVP and Mobility-Aware RSVP Using Performance Evaluation Process Algebra
As a resource reservation mechanism, the Resource ReSerVation Protocol (RSVP) faces a lot of challenges when applying it to the wireless and mobile networks. The interworking problems of RSVP and mobility management protocols have been extensively discussed over the last decade. As the solutions of this problem, mobility-aware RSVP schemes that integrate RSVP and micro-mobility management are becoming more and more popular. Therefore, the investigation on how much they improve the performance of the basic RSVP is necessary and useful. Instead of the traditional simulation based approaches, in this paper we introduce a formal performance evaluation formalism, named Performance Evaluation Process Algebra (PEPA), and employ it to investigate the performance of the basic RSVP and mobility-aware RSVP. Important performance metrics such as handover blocking probability and signalling cost are presented.