A survey of hierarchical classification across different application domains

Abstract

In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.

Can computer vision problems benefit from structured hierarchical classification?

“…Recently, a necessary effort to unify the hierarchical classification framework has been made [28]. We follow on their terminology which is summarized next.…”

“…Most of the theoretical work and applications in the field of supervised classification have been dedicated to the standard classification approach, where the problem classes are considered to be equally different from each other in a semantic sense [28]. In this standard approach, also known as "flat" classification, a classifier is learned from class-labeled data instances without any explicit information given about the high-level semantic relationships between the classes.…”

“…However, one could consider that ants and bees are part of a superclass of insects, while hammers belong to another superclass of tools, and it is intuitive that such hierarchical knowledge about the classes can help improve the classification performances. Based upon this realization, a new approach has emerged for dealing more efficiently with classification of content deemed to be inherently semantically hierarchical, i.e., the hierarchical classification approach [28]. The attention given to the hierarchical approach was also sustained by the advances made in machine learning generalized to arbitrary output spaces, i.e., the structured classification approach (e.g., [31]), of which the hierarchical approach is actually a special case.…”

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

“…Many real-world classification problems have hierarchical classes, were a case belongs to a series of classes related in a general-to-specific structure. Such class structure is found, e.g., in document topics, music genres and protein functions, which makes the classification task more complex and challenging [19]. Figure 1 shows examples of hierarchical classification problems.…”

“…There are three different broad approaches to tackle hierarchical classification problems [19]. The first (and the simplest) approach is to completely ignore the class hierarchy and convert the problem into flat classification by predicting only classes at the leaf nodes.…”