From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique

For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity. Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal Pain, a group of exclusion

Survey on Classification Algorithms for Data Mining:(Comparison and Evaluation)

Data mining concept is growing fast in popularity, it is a technology that involving methods at the intersection of (Artificial intelligent, Machine learning, Statistics and database system), the main goal of data mining process is to extract information from a large data into form which could be understandable for further use. Some algorithms of data mining are used to give solutions to classification problems in database. In this paper a comparison among three classification’s algorithms will be studied, these are (K- Nearest Neighbor classifier, Decision tree and Bayesian network) algorithms. The paper will demonstrate the strength and accuracy of each algorithm for classification in term of performance efficiency and time complexity required. For model validation purpose, twenty-four-month data analysis is conducted on a mock-up basis. Keywords: Decision tree, Bayesian network, k- nearest neighbour classifier

A comparative study of selected classification accuracy in user profiling

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs)

Active Learning with Multiple Views

Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing