Naive bayes classification of uncertain data

Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification algorithm for uncertain data with a pdf. Our key solution is to extend the class conditional probability estimation in the Bayes model to handle pdf's. Extensive experiments on UCI datasets show that the accuracy of naive Bayes model can be improved by taking into account the uncertainty information. © 2009 IEEE.published_or_final_versionThe 9th IEEE International Conference on Data Mining (ICDM), Miami, FL., 6-9 December 2009. In Proceedings of the 9th ICDM, 2009, p. 944-94

An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data

Coping with uncertainty is a very challenging issue in many real-world applications. However, conventional classification models usually assume there is no uncertainty in data at all. In order to fill this gap, there has been a growing number of studies addressing the problem of classification based on uncertain data. Although some methods resort to ignoring uncertainty or artificially removing it from data, it has been shown that predictive performance can be improved by actually incorporating information on uncertainty into classification models. This paper proposes an approach for building an ensemble of classifiers for uncertain categorical data based on biased random subspaces. Using Naive Bayes classifiers as base models, we have applied this approach to classify ageing-related genes based on real data, with uncertain features representing protein-protein interactions. Our experimental results show that models based on the proposed approach achieve better predictive performance than single Naive Bayes classifiers and conventional ensembles

Named Entity Extraction and Disambiguation: The Reinforcement Effect.

Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u

Node Classification in Uncertain Graphs

In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process. If the information about link reliability is not used explicitly, the classification accuracy in the underlying network may be affected adversely. In this paper, we focus on situations that require the analysis of the uncertainty that is present in the graph structure. We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen. We propose two techniques based on a Bayes model and automatic parameter selection, and show that the incorporation of uncertainty in the classification process as a first-class citizen is beneficial. We experimentally evaluate the proposed approach using different real data sets, and study the behavior of the algorithms under different conditions. The results demonstrate the effectiveness and efficiency of our approach