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Data analysis plays an important role in our InformationEra; however, most of statistical and machine learning algorithms were not developed to tackle the ubiquitous issue of missing values. In pattern classification, several strategies have been proposed to handle this problem, where missing data imputation is the most used one, which can be viewed as an optimization problem where the goal is to reduce the bias imposed by the absence of information. Although most imputation methods are restricted to one type of variable only (categorical or numerical), they usually ignore information within incomplete instances. To fill these gaps, we propose an evolutionary missing data imputation method for pattern classification, based on a genetic algorithm, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and model building -more specifically, the classification accuracy. To assess the performance of our method, we used three algorithms in order to represent the three groups of classification methods: 1) rule induction learning, 2) approximate models and 3) lazy learning. Experiments have shown that the proposed method outperforms some well-established missing value treatment methods.

Self-organized wireless networks comprise different devices cooperating among themselves to provide network services. These networks support critical applications in different domains requesting simultaneously robustness, security and availability. Hence, it is essential to develop mechanisms for steering nodes' cooperation in order to preserve essential network operations even in presence of intrusion, attacks or network failures. We propose a trust management framework whose goal is to guide node cooperation towards network survivability. Our framework evaluates node trustworthiness correlating adaptively criteria provided by node behavior, recommendations, security mechanisms and node attributes. Simulation results show improvements on network trustworthiness for different situations.

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