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A Comparative Study Between Local and Global Approaches for Hierarchical Classification

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Title: A Comparative Study Between Local and Global Approaches for Hierarchical Classification
Author(s): Seth, Arpita
Contributor: University of Helsinki, Faculty of Science, Tietojenkäsittelytieteen osasto
Discipline: Algorithms and Machine Learning
Language: English
Acceptance year: 2020
Traditional flat classification methods (e.g., binary, multiclass, and multi-label classification) seek to associate each example with a single class label or a set of labels without any structural dependence among them. Although, there are some problems in which classes can be divided or grouped into subclasses or superclasses respectively. Such a scenario demands the application of methods prepared to deal with hierarchical classification. An algorithm for hierarchical classification uses the information related to structure present in the class hierarchy and then improves the predictive performance . The freedom to perform a more generic classification, but with higher reliability, gives the process a greater versatility. Several studies have shown that, in solving a hierarchical classification problem, flat models are mostly overcome by hierarchical ones, regardless of the approach – local (including its derivations) or global – chosen. This thesis aims to compare the most popular hierarchical classification methods (local and global) empirically, reporting their performance – measured using hierarchical evaluation indexes. To do so, we had to adapt the global hierarchical models to conduct single path predictions, starting from the root class and moving towards a leaf class within the hierarchical structure. Further, we applied hierarchical classification on data streams by detecting concept drift. We first study data streams, various types of concept drifts, and state-of-the-art concept drift detection methods. Then we implement Global-Model Hierarchical Classification Naive Bayes (GNB) with three concept drift detectors: (i) Kolmogorov-Smirnov test, (ii) Wilcoxon test, and (iii) Drift Detection Method (DDM). A fixed-size sliding window was used to estimate the performance of GNB online. Finally, we must highlight that this thesis contributes to the task of automatic insect recognition.
Keyword(s): Hierarchical Classification Global Naive Bayes CLUS HMC Concept Drift KS Test Wilcox DDM

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