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An adaptive personalized news dissemination system 总被引:1,自引:0,他引:1
Ioannis Katakis Grigorios Tsoumakas Evangelos Banos Nick Bassiliades Ioannis Vlahavas 《Journal of Intelligent Information Systems》2009,32(2):191-212
With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor
environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became
a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems
are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced
with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to
examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification
of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news
sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content
not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted
topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW ().
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Ioannis VlahavasEmail: |
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This paper proposes a new measure for ensemble pruning via directed hill climbing, dubbed Uncertainty Weighted Accuracy (UWA),
which takes into account the uncertainty of the decision of the current ensemble. Empirical results on 30 data sets show that
using the proposed measure to prune a heterogeneous ensemble leads to significantly better accuracy results compared to state-of-the-art
measures and other baseline methods, while keeping only a small fraction of the original models. Besides the evaluation measure,
the paper also studies two other parameters of directed hill climbing ensemble pruning methods, the search direction and the
evaluation dataset, with interesting conclusions on appropriate values. 相似文献
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Tracking recurring contexts using ensemble classifiers: an application to email filtering 总被引:3,自引:3,他引:0
Ioannis Katakis Grigorios Tsoumakas Ioannis Vlahavas 《Knowledge and Information Systems》2010,22(3):371-391
Concept drift constitutes a challenging problem for the machine learning and data mining community that frequently appears
in real world stream classification problems. It is usually defined as the unforeseeable concept change of the target variable
in a prediction task. In this paper, we focus on the problem of recurring contexts, a special sub-type of concept drift, that has not yet met the proper attention from the research community. In the case
of recurring contexts, concepts may re-appear in future and thus older classification models might be beneficial for future
classifications. We propose a general framework for classifying data streams by exploiting stream clustering in order to dynamically
build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples
into a new conceptual representation model is proposed. The clustering algorithm is then applied in order to group batches of examples into concepts
and identify recurring contexts. The ensemble is produced by creating and maintaining an incremental classifier for every
concept discovered in the data stream. An experimental study is performed using (a) two new real-world concept drifting datasets
from the email domain, (b) an instantiation of the proposed framework and (c) five methods for dealing with drifting concepts.
Results indicate the effectiveness of the proposed representation and the suitability of the concept-specific classifiers
for problems with recurring contexts. 相似文献
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Mollas Ioannis Bassiliades Nick Tsoumakas Grigorios 《Data mining and knowledge discovery》2022,36(4):1521-1574
Data Mining and Knowledge Discovery - In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be... 相似文献
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