Combining Prototype Selection with Local Boosting
Real life classification problems require an investigation of relationships between features in heterogeneous data sets, where different predictive models can be more proper for different regions of the data set. A solution to this problem is the applicat
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Abstract. Real life classification problems require an investigation of relationships between features in heterogeneous data sets, where different predictive models can be more proper for different regions of the data set. A solution to this problem is the application of the local boosting of weak classifiers ensemble method. A main drawback of this approach is the time that is required at the prediction of an unseen instance as well as the decrease of the classification accuracy in the presence of noise in the local regions. In this research work, an improved version of the local boosting of weak classifiers, which incorporates prototype selection, is presented. Experimental results on several benchmark real-world data sets show that the proposed method significantly outperforms the local boosting of weak classifiers in terms of predictive accuracy and the time that is needed to build a local model and classify a test instance.
Keywords: Local boosting Pattern classification
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Weak learning
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Prototype selection
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Introduction
In machine learning, instance-based (or memory-based) learners classify an unseen object by comparing it to a database of pre-classified objects. The fundamental assumption is that similar instances will share similar class labels. Machine learning models’ assumptions would not necessarily hold globally. Local learning [1] methods come to solve this problem. The latter allow to extend learning algorithms, that are designed for simple models, to the case of complex data, for which the models’ assumptions are valid only locally. The most common case is the assumption of linear separability, which is usually not fulfilled globally in classification problems. Despite this, any supervised learning algorithm that is able to find only a linear separation, can be used inside a local learning process, producing a model that is able to model complex non-linear class boundaries. A technique of boosting local weak classifiers, that is based on a reduced training set after the usage of prototype selection [11], is proposed. It is common that boosting algorithms are well-known to be susceptible to noise [2]. In the case c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 94–105, 2016. DOI: 10.1007/978-3-319-44944-9 9
Combining Prototype Selection with Local Boosting
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of local boosting, the algorithm should manage reasonable noise and be at least as good as boosting, if not better. For the experiments, we used two variants of Decision Trees [21] as weak learning models: one-level Decision Trees, which are known as Decision Stumps [12] and two-level Decision Trees. An extensive comparison over several data sets was performed and the results show that the proposed method outperforms simple and local boosting in terms of classification accuracy. In the next Section, specifically in Subsect. 2.1, the localized experts are discussed, while boost
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