2 research outputs found
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