44335940
DRC-BK: Mining Classification Rules with Help of SVM
Currently, the accuracy of SVM classifier is very high, but the classification model of SVM classifier is not understandable by human experts. In this paper, we use SVM, which is applied with a Boolean kernel, to construct a hyper-plan for classification, and mine classification rules from this hyper-plane. In this way, we build DRC-BK, a decision rule classifier. Experiment results show that DRC-BK has a higher accuracy than some state-of-art decision rule (decision tree) classifiers, such as C4.5, CBA, CMAR, CAEP and so on.
40790501
A Practical Peer-Performance-Aware DHT
How to Build an efficient Distributed Hash Table (DHT) is a fundamental issue in Peer-to-Peer research field. Previous solutions ignore the heterogeneity of the large scale network. However, in practice, the fact is that the resource held by each peer in the Internet is extremely diverse. And the the willing to share local resources of each peer is also diverse. Therefore, the contribution for the system of a peer should depend on the resources it holds or how many resources it want to share, and should not be uniform. In this paper, we propose a Peer-Performance-Aware Distributed Hash Table (PPADHT) which aims to exploit the heterogeneity. It takes the performance difference of peers into consideration to construct a dynamic variation of wrapped butterfly to achieve the goal. We also show how to optimize the performance of PPADHT in the view of hop counts by random graphs. Our simulation results show that the average lookup hop counts of the PPADHT is approximately a log scale with constant out degrees. And it can achieve loadbalance in two ways: both the document load and message routing load, without introducing any additional load on the peer. Here, the load balance means the load is proportion to the performance of peer.
17436215
Organizing Meaning Evolution Supporting Systems Using Semantic Decision Tables
DOGMA-MESS (Meaning Evolution Support System) is a system and methodology for supporting scalable, community-grounded ontology engineering. It uses a socio-technical process of meaning negotiation to tackle the scalability problems in ontology engineering. In order to improve the effectiveness of DOGMA-MESS, we adopt the idea of Semantic Decision Table (SDT). An SDT contains semantically rich decision rules that guide DOGMA-MESS micro-processes. It separates the decision rules from DOGMA-MESS. SDT with different decision rules results in different final decisions, which can be evaluated. In this paper, we illustrate how SDTs are used and apply our approach in the domain of Human Resource Management.
1361502
Model Ontological Commitments Using ORM+ in T-Lex
When designing and developing ontology based applications, we semantically ground them by ontologically committing the application rules to their respective domain. These rules can be, for instance decision rules for a decision support system. For the DOGMA framework we have introduced ORM + , a novel extension of ORM for modeling, visualizing and interchanging ontological commitments. In this paper, we illustrate our ongoing research on ORM + and T-Lex as its supporting tool. We demonstrate in the field of on-line customer management.
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.
22140835
The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition
A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique is presented. The R peaks are then identified by computing the first threshold and applying a set of adaptive thresholds that are not limited to a strict range. The proposed algorithm was able to detect R peak with high sensitivity when compared to other algorithms used in Physionet ECG database. Additionally, the new algorithm can successfully detect R peak for a wide variety of ECG shapes.
29100810
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
The importance of requirements engineering (RE) has been raised numerous times in literatures. To choose suitable RE techniques for a particular project in a given situation is a challenging task, requiring substantial expertise and efforts. To help solving this problem, an expert system based approach is proposed. This expert system uses the knowledge from domain experts to model the causal factors of RE techniques. It can select suitable RE techniques for a software project. A web-based questionnaire is created in the first place to collect the expertise available in the community. The information collected by the questionnaire is analyzed and transformed into a new dataset for constructing a Bayesian Belief Network (BBN). The resulting BBN integrated with a GUI forms an expert system for RE techniques modeling and selection. Empirical study validates the transformed dataset and shows that the expert system outperforms other predictors in selecting suitable RE techniques in different RE phases.