HierCost: Improving Large Scale Hierarchical Classification with Cost Sensitive Learning
Hierarchical Classification (HC) is an important problem with a wide range of application in domains such as music genre classification, protein function classification and document classification. Although several innovative classification methods have b
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Abstract. Hierarchical Classification (HC) is an important problem with a wide range of application in domains such as music genre classification, protein function classification and document classification. Although several innovative classification methods have been proposed to address HC, most of them are not scalable to web-scale problems. While simple methods such as top-down “pachinko” style classification and flat classification scale well, they either have poor classification performance or do not effectively use the hierarchical information. Current methods that incorporate hierarchical information in a principled manner are often computationally expensive and unable to scale to large datasets. In the current work, we adopt a cost-sensitive classification approach to the hierarchical classification problem by defining misclassification cost based on the hierarchy. This approach effectively decouples the models for various classes, allowing us to efficiently train effective models for large hierarchies in a distributed fashion.
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Introduction
Categorizing entities according to a hierarchy of general to specific classes is a common practice in many disciplines. It can be seen as an important aspect of various fields such as bioinformatics, music genre classification, image classification and more importantly document classification [18]. Often the data is curated manually, but with exploding sizes of databases, it is becoming increasingly important to develop automated methods for hierarchical classification of entities. Several classification methods have been developed over the past several years to address the problem of Hierarchical Classification (HC). One straightforward approach is to simply use multi-class or binary classifiers to model the relevant classes and disregard the hierarchical information. This methodology has been called flat classification scheme in HC literature [18]. While flat classification can be competitive, an important research directions is to improve the classification performance by incorporating the hierarchical structure of the classes in the learning algorithm. Another simple data decomposition approach trains local classifiers for each of the classes defined according to the hierarchy, such that the trained model can be used in a top-down fashion to take the most relevant path in testing. This top-down approach trains each classifier on a smaller dataset and c Springer International Publishing Switzerland 2015 A. Appice et al. (Eds.): ECML PKDD 2015, Part I, LNAI 9284, pp. 675–690, 2015. DOI: 10.1007/978-3-319-23528-8 42
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A. Charuvaka and H. Rangwala
is quite efficient in comparison to flat classification, which generally train one-vsrest classifiers on the entire dataset. However, a severe drawback of this approach is that if a prediction error is committed at a higher level, then the classifier selects a wrong prediction path, making it impossible to recover from the errors at lower levels. Due to this error propagation, sometimes, sever degradation in performance has been noted for the top-down cl
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