The Advantage of Evidential Attributes in Social Networks

Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an, Chin

Handling Uncertain Attribute Values In Decision Tree Classifier Using The Belief Function Theory

International audienceDecision trees are regarded as convenient machine learning techniques for solving complex classification problems. However, the major shortcoming of the standard decision tree algorithms is their unability to deal with uncertain environment. In view of this, belief decision trees have been introduced to cope with the case of uncertainty present in class' value and represented within the belief function framework. Since in various real data applications, uncertainty may also appear in attribute values, we propose to develop in this paper another version of decision trees in a belief function context to handle the case of uncertainty present only in attribute values for both construction and classification phases