Zhiqiu Huang

Three-way decisions based software defect prediction

Based on a two-stage classification method and a two-stage ranking method on three-way decisions, this paper introduces a three-way decisions framework for cost-sensitive software defect prediction. For the classification problem in software defect prediction, traditional two-way decisions methods usually generate a higher classification error and more decision cost. Here, a two-stage classification method that integrates three-way decisions and ensemble learning to predict software defect is proposed. Experimental results on NASA data sets show that our method can obtain a higher accuracy and a lower decision cost. For the ranking problem in software defect prediction, a two-stage ranking method is introduced. In the first stage, all software modules are classified into three different regions based on three-way decisions. A dominance relation rough set based ranking algorithm is next applied to rank the modules in each region. Comparison experiments with 6 other ranking methods present that our proposed method can obtain a better result on FPA measure.

Neighborhood based decision-theoretic rough set models

Abstract As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS) adopts the Bayesian decision theory to compute the required thresholds in probabilistic rough set models. It gives a new semantic interpretation of the positive, boundary and negative regions by using three-way decisions. DTRS has been widely discussed and applied in data mining and decision making. However, one limitation of DTRS is its lack of ability to deal with numerical data directly. In order to overcome this disadvantage and extend the theory of DTRS, this paper proposes a neighborhood based decision-theoretic rough set model (NDTRS) under the framework of DTRS. Basic concepts of NDTRS are introduced. A positive region related attribute reduct and a minimum cost attribute reduct in the proposed model are defined and analyzed. Experimental results show that our methods can get a short reduct. Furthermore, a new neighborhood classifier based on three-way decisions is constructed and compared with other classifiers. Comparison experiments show that the proposed classifier can get a high accuracy and a low misclassification cost.

Conceptual modeling rules extracting for data streams

Data take the form of continuous data streams rather than traditional stored databases in a growing number of applications, including network traffic monitoring, network intrusion detection, sensor networks, fraudulent transaction detection, financial monitoring, etc. People are interested in the potential rules in data streams such as association rules and decision rules. Compared with much work on developing algorithms of data streams mining, there is little attention paid on the modeling data mining and data streams mining. Considering the problem of conceptual modeling data streams mining, we put forward a data streams oriented decision logic language as a granular computing formal approach and a rules extracting model based on granular computing. In this model, we propose the notion of granular drifting, which accurately interpret the concept drifting problem in data streams. This model is helpful to understand the nature of data streams mining. Based on this model, new algorithms and techniques of data streams mining could be developed.

Self-adaptive semantic web service matching method

Web service has become a major software paradigm and computing resource, while how to implement web service matching also has become a key issue. In this paper, we present a self-adaptive semantic web service matching method, which improves the precision and recall of service discovery. In this method, requirement document and service profile ontology of OWL-S are transformed into ontology trees respectively. Conception similarity, attribute similarity and structure similarity of corresponding nodes in trees are calculated through taxonomic and hierarchical methodology. Then a serial of constraints are defined according to the relationship between conception similarity and structure similarity, to get the corresponding restructure rules. By restructuring requirement ontology tree in self-adaptive way, we achieve more accurate destination service collections. In the end, we propose matching algorithm of semantic web service and implement prototype system of OWLS-CPS. We prove the feasibility and effectiveness through evaluating and comparing to the OWLS-M4.

Supporting negotiation mechanism privacy authority method in cloud computing

Cloud computing have become a software paradigm, providing services dynamically according to user requirements. However, it is difficult to control personal privacy information because of the opening, virtualization, multi-tenancy and service outsourcing characters. Therefore how to protect user privacy information has become a research focus in cloud computing. Considering the service outsourcing character, we propose a privacy information description method and negotiation mechanism. Firstly, we describe privacy property with Privacy Negotiation Language (PNL) based on description logic. Secondly, we get privacy attribute sequence through pre-negotiation between user and service composer. Thirdly, though exchanging privacy disclosure assertion, we obtain privacy policy that satisfying both parties. In the end, we put forward privacy policy negotiation algorithm. Through case study we proved the feasibility and correctness of this method.

A Semantic Model for Matchmaking of Web Services Based on Description Logics

Matchmaking plays an important role in Web services interactions. The matchmaking based on keywords easily leads to low precision, Meanwhile, the current semantic service discovery methods perform service I/O based profile matching, there exists no matchmaker that performs an integrated service matching by additional reasoning on logically defined preconditions, effects, Qos and so on. In this paper, the semantic web services are described based on Description Logics, and the services description model is designed, which describes the various aspects (such as IOPEs, Qos and so on) of the web services. So the services matchmaking is transformed into the match of concepts. The service match algorithm is proposed and the description logics reasoner RacerPro is adopted for Web services discovery. We show how the semantic matching between providers and a requester is performed by a case study.

Description logic based consistency checking upon data mining metadata

During the process of constructing data mining metadata, the evolution of data mining techniques, the different experiences and views of related organizations inevitably cause inconsistencies. However, current data mining metadata lacks precise semantic due to their description with natural language and graphs, so the automatic consistency checking upon them has not been resolved well. In this paper, a formal logic DLRDM in the description logic family is proposed. Subsequently, a formal reasoning method based on DLRDM is designed to automatically check the consistency of data mining metadata. With the description logic DLRDM, formalization upon the metamodel and metadata of data mining is analyzed in detail. The reasoning engine Racer is applied into the method to check the consistency upon the data mining metadata. Results on the RacerPro reasoning system indicate the method is encouraging.

Service Outsourcing Character Oriented Privacy Conflict Detection Method in Cloud Computing

Cloud computing has provided services for users as a software paradigm. However, it is difficult to ensure privacy information security because of its opening, virtualization, and service outsourcing features. Therefore how to protect user privacy information has become a research focus. In this paper, firstly, we model service privacy policy and user privacy preference with description logic. Secondly, we use the pellet reasonor to verify the consistency and satisfiability, so as to detect the privacy conflict between services and user. Thirdly, we present the algorithm of detecting privacy conflict in the process of cloud service composition and prove the correctness and feasibility of this method by case study and experiment analysis. Our method can reduce the risk of user sensitive privacy information being illegally used and propagated by outsourcing services. In the meantime, the method avoids the exception in the process of service composition by the privacy conflict, and improves the trust degree of cloud service providers.

Two-Phase Classification Based on Three-Way Decisions

A two-phase classification method is proposed based on three-way decisions. In the first phase, all objects are classified into three different regions by three-way decisions. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. The positive region contains those objects that have been assigned a class label with a high level of confidence. The boundary and negative regions contain those objects that have not been assigned class labels. In the second phase, a simple ensemble learning approach to determine the class labels of objects in the boundary or negative regions. Experiments are performed to compare the proposed two-phase classification approach and a classical classification approach. The results show that our method can produce a better classification accuracy than the classical model.

Obtaining P3P privacy policies for composite services.

With the development of web services technology, web services have changed from single to composite services. Privacy protection in composite services is becoming an important issue. P3P (platform for privacy preferences) is a privacy policy language which was designed for single web services. It enables service providers to express how they will deal with the privacy information of service consumers. In order to solve the problem that P3P cannot be applied to composite services directly, we propose a method to obtain P3P privacy policies for composite services. In this method, we present the definitions of Purpose, Recipient, and Retention elements as well as Optional and Required attributes for P3P policies of composite services. We also provide an instantiation to illustrate the feasibility of the method.