Boosting Algorithm with Sequence-Loss Cost Function for Structured Prediction

Kajdanowicz, Tomasz; Kazienko, Przemysław; Kraszewski, Jan

doi:10.1007/978-3-642-13769-3_70

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2010

2024

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Cited by 5 publications

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“…Additionally, the proposed feature extraction method will also be examined against the usage of global objective functions for classification. Yet another direction of future studies will be development of new ensemble algorithms, which would have network measures already incorporated, especially based on boosting concept [8].…”

Section: Discussionmentioning

confidence: 99%

Label-Dependent Feature Extraction in Social Networks for Node Classification

Kajdanowicz

Kazienko

Doskocz

2010

Lecture Notes in Computer Science

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A new method of feature extraction in the social network for withinnetwork classification is proposed in the paper. The method provides new features calculated by combination of both: network structure information and class labels assigned to nodes. The influence of various features on classification performance has also been studied. The experiments on realworld data have shown that features created owing to the proposed method can lead to significant improvement of classification accuracy.

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Section: Discussionmentioning

confidence: 99%