10482170
Support vector machines with genetic fuzzy feature transformation for biomedical data classification
Abstract In this paper, we present a genetic fuzzy feature transformation method for support vector machines (SVMs) to do more accurate data classification. Given data are first transformed into a high feature space by a fuzzy system, and then SVMs are used to map data into a higher feature space and then construct the hyperplane to make a final decision. Genetic algorithms are used to optimize the fuzzy feature transformation so as to use the newly generated features to help SVMs do more accurate biomedical data classification under uncertainty. The experimental results show that the new genetic fuzzy SVMs have better generalization abilities than the traditional SVMs in terms of prediction accuracy.
14388340
Granular support vector machines with association rules mining for protein homology prediction
OBJECTIVE
Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions.


METHODOLOGY
A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified.


RESULTS AND CONCLUSIONS
The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.
39730181
The Binary Multi-SVM Voting System for Protein Subcellular Localization Prediction
Support Vector Machine (SVM) as a learning system has been widely employed for pattern recognition and data classification tasks such as biological data classification. Choosing appropriate parameters are essential for SVM to achieve a high global performance. In this paper, we propose a new binary multi-SVM voting system without difficult parameter selection for protein subcellular localization prediction. The sufficient experimental results demonstrate that the multi-SVM voting system can achieve higher average prediction accuracies for the protein subcellular localization prediction than the traditional single-SVM system.
9633153
Fuzzy-Granular Gene Selection from Microarray Expression Data
Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the fuzzy C-means algorithm (FCM). And then informative genes in each cluster are selected with the signal to noise metric (S2N). With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. The simulation results on two publicly available microarray expression datasets show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies
40999006
A δ-shock maintenance model for a deteriorating system
Abstract In this paper, a δ -shock maintenance model for a deteriorating system is studied. Assume that shocks arrive according to a renewal process, the interarrival time of shocks has a Weibull distribution or gamma distribution. Whenever an interarrival time of shocks is less than a threshold, the system fails. Assume further the system is deteriorating so that the successive threshold values are geometrically nondecreasing, and the consecutive repair times after failure form an increasing geometric process. A replacement policy N is adopted by which the system will be replaced by an identical new one at the time following the N th failure. Then the long-run average cost per unit time is evaluated. Afterwards, an optimal policy N * for minimizing the long-run average cost per unit time could be determined numerically.
27720720
Finding a maximum induced matching in weakly chordal graphs
An induced matching in a graph G is a set of edges, no two of which meet a common vertex or are joined by an edge of G; that is, an induced matching is a matching which forms an induced subgraph. It is known that finding an induced matching of maximum cardinality in a graph is NP-hard. We show that a maximum induced matching in a weakly chordal graph can be found in polynomial time. This generalizes previously known results for the induced matching problem. This also demonstrates that the maximum induced matching problem in chordal bipartite graphs can be solved in polynomial time while the problem is known to be NP-hard for bipartite graphs in general.
42391014
QoS Path Monitoring for Multicast Networks
New network monitoring tools are necessary for supporting the deployment and the operations of multicast services on the Internet. Because of the peculiar characteristics of multicast routing (e.g., multicast forwarding trees) and the potential of message implosion problems, traditional network management tools are not sufficient for monitoring the quality of multicast delivery such as packet loss, delay, and jitter. Current multicast monitoring tools are either not scalable, limited in their functionality, or difficult to deploy in enterprise networks.In this paper, we present a new monitoring framework (called SMRM, SNMP-based multicast reachability monitoring) for multicast reachability based on SNMP. The SMRM framework is used for actively monitoring the health and the quality of service of multicast networks. SMRM provides a scalable real-time feedback on the packet loss, delay, and jitter of any selected segments of multicast delivery trees. In addition, NOC (network operations center) personnel can easily understand, deploy, and extend the SMRM framework in order to detect and isolate reachability and performance problems in multicast sessions. SMRM combines both distributed monitoring and centralized control, which offers scalability and simplicity. The integration of SMRM into SNMP is motivated by the wide distribution of SNMP agents in networks today, which significantly facilitates the deployment of SMRM is existing networks.
17121861
Active integrated fault localization in communication networks
Fault localization is a core element in fault management. Many fault reasoning techniques use deterministic or probabilistic symptom-fault causality model for fault diagnoses and localization. Symptom-fault map is commonly used to describe symptom-fault causality in fault reasoning. However, due to lost and spurious symptoms in fault reasoning systems that passively collect symptoms, the performance and accuracy of the fault localization can be significantly degraded. In this paper, we propose an extended symptom-fault-action model to incorporate actions into fault reasoning process to tackle the above problem. This technique is called active integrated fault reasoning (AIR), which contains three modules: fault reasoning, fidelity evaluation and action selection. Corresponding fault reasoning and action selection algorithms are elaborated. Simulation study shows both performance and accuracy of fault reasoning can be greatly improved by taking actions, especially when the rate of spurious and lost symptoms is high.
2555681
Judicial Support Systems: Ideas for a Privacy Ontology-Based Case Analyzer
Nowadays, ontology is applied as an integral part of many applications in several domains, especially in the world of law. The ontology based judicial support system is believed as a useful tool to support, for example, the legal argumentation assistant and legal decision taking in court. The privacy case analyzer is considered as one of the most interesting applications of ontology based privacy judicial support systems. The efficiency of privacy case analyzers depend on several factors such as how to tackle the problem of linking cases to legislations, how to imply the guidance of privacy principles, and how to improve the extraction of cases. This paper addresses those items and describes the research issues that will be investigated challenges of ontology based judicial support systems.