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Hierarchical classification for multiple, distributed web databases
The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance
Discriminating word senses with tourist walks in complex networks
Patterns of topological arrangement are widely used for both animal and human
brains in the learning process. Nevertheless, automatic learning techniques
frequently overlook these patterns. In this paper, we apply a learning
technique based on the structural organization of the data in the attribute
space to the problem of discriminating the senses of 10 polysemous words. Using
two types of characterization of meanings, namely semantical and topological
approaches, we have observed significative accuracy rates in identifying the
suitable meanings in both techniques. Most importantly, we have found that the
characterization based on the deterministic tourist walk improves the
disambiguation process when one compares with the discrimination achieved with
traditional complex networks measurements such as assortativity and clustering
coefficient. To our knowledge, this is the first time that such deterministic
walk has been applied to such a kind of problem. Therefore, our finding
suggests that the tourist walk characterization may be useful in other related
applications
Rationale in Development Chat Messages: An Exploratory Study
Chat messages of development teams play an increasingly significant role in
software development, having replaced emails in some cases. Chat messages
contain information about discussed issues, considered alternatives and
argumentation leading to the decisions made during software development. These
elements, defined as rationale, are invaluable during software evolution for
documenting and reusing development knowledge. Rationale is also essential for
coping with changes and for effective maintenance of the software system.
However, exploiting the rationale hidden in the chat messages is challenging
due to the high volume of unstructured messages covering a wide range of
topics. This work presents the results of an exploratory study examining the
frequency of rationale in chat messages, the completeness of the available
rationale and the potential of automatic techniques for rationale extraction.
For this purpose, we apply content analysis and machine learning techniques on
more than 8,700 chat messages from three software development projects. Our
results show that chat messages are a rich source of rationale and that machine
learning is a promising technique for detecting rationale and identifying
different rationale elements.Comment: 11 pages, 6 figures. The 14th International Conference on Mining
Software Repositories (MSR'17
Naive bayes multi-label classification approach for high-voltage condition monitoring
This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMI time signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sources contained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI discharge sources opens a new research topic in HV condition monitoring
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