Advisor(s)
Abstract(s)
In this paper we propose and analyse methods for expanding state-of-the-art performance on text classification. We put forward an ensemble-based structure that includes Support Vector Machines (SVM) and Artificial Immune Systems (AIS). The underpinning idea is that SVM-like approaches can be enhanced with AIS approaches which can capture dynamics in models. While having radically different genesis, and probably because of that, SVM and AIS can cooperate in a committee setting, using a heterogeneous ensemble to improve overall performance, including a confidence on each system classification as the differentiating factor.
Results on the well-known Reuters-21578 benchmark are presented, showing promising classification performance gains, resulting in a classification that improves upon all baseline contributors of the ensemble committee.
Description
Keywords
Artificial Immune System Support Vector Machine Text Classification Tunable Activation Threshold Ensembles Hybrid System.
Pedagogical Context
Citation
Antunes, M., Silva, C., Ribeiro, B., Correia, M. (2011). A Hybrid AIS-SVM Ensemble Approach for Text Classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_36
Publisher
Springer Berlin Heidelberg