Browsing by Author "Ribeiro, Bernardete"
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- Adaptive learning for dynamic environments: A comparative approachPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteNowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn++.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn++.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).
- Improving Text Classification Performance with Incremental Background KnowledgePublication . Silva, Catarina; Ribeiro, BernardeteText classification is generally the process of extracting interesting and non-trivial information and knowledge from text. One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we propose an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to deliver oracle decisions. The defined incremental SVM margin-based method was tested in the Reuters-21578 benchmark showing promising results.
- Knowledge Extraction with Non-Negative Matrix Factorization for Text ClassificationPublication . Silva, Catarina; Ribeiro, BernardeteText classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness.