An Effective Ensemble Approach for Spam Classification

The annoyance of spam increasingly plagues both individuals and organizations. Spam classification is an important issue to distinguish the spam with the legitimate email or address. This paper presents a neural network ensemble approach based on a specially designed cooperative coevolution paradigm. Each component network corresponds to a separate subpopulation and all subpopulations are evolved simultaneously. The ensemble performance and the Q-statistic diversity measure are adopted as the objectives, and the component networks are evaluated by using the multi-objective Pareto optimality measure. Experimental results illustrate that the proposed algorithm outperforms the traditional ensemble methods on the spam classification problems

Predicting Requirements Change Propagation Based on Software Architecture

Change propagation is a central issue in software development process. In early stages of software development, software architecture facilitates the component-based software development process and provides a platform for prediction of requirements change. Keywords: software architecture, requirements change propagation, architectural pattern This paper aims to predict change propagation in early stages of software development, and evaluate the architecture based on architectural pattern. In order to achieve this goal, the change propagation probability is formally defined, and the change propagation in five architectural patterns is discussed. Moreover, change propagation density is defined to extend the pattern-based propagation, which incorporates design metrics into software architecture evaluation. The efficiency of the proposed method is demonstrated through a computational experiment

Method for Web Service Composition Discovery Based on Association Rules

Web service plays an important role in implementing Service Oriented Architecture (SOA) for achieving dynamic business process. With the increased number of web services advertised in public repository, it is becoming vital to provide an efficient web service composition mechanism with respect to user’s requirement. In this paper, a service composition approach based on association rules is proposed in the sense of knowledge discovery. This approach includes two steps: firstly, frequent web services will be enumerated in the dataset of history service composition transactions. Secondly, execution path, the basic unit of composite service, will be generated based on concepts extracted from SOA domain and association rules implied in frequent services. In addition, frequent services mining algorithm is put forward based on a structure of Adjacent-Lattice, and experiments are given to show the effectiveness of the mining algorithm