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Hierarchical Classification With Bayesian Networks and Chained Classifiers

AAAI Conferences

In this work is proposed a method for Hierarchical Classification, which takes advantage of the hierarchical structure to influence the prediction of local classifiers with their neighbors. To achieve this, two strategies are combined. The first is to represent the hierarchical structure as a Bayesian network, and the second is to build chained classifiers that feed the Bayesian network as local classifiers. The proposed method was tested in several datasets of functional genomics, which consist of tree-structured hierarchies. The results of several variants of the proposed method are compared to the standard methods, Flat and Top-Down, as well as with a start of the art technique, showing superior performance under several metrics.


Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification

Neural Information Processing Systems

In hierarchical classification, the prediction paths may be required to always end at leaf nodes. This is called mandatory leaf node prediction (MLNP) and is particularly useful when the leaf nodes have much stronger semantic meaning than the internal nodes. However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is much more difficult. In this paper, we propose a novel MLNP algorithm that (i) considers the global hierarchy structure; and (ii) can be used on hierarchies of both trees and DAGs. We show that one can efficiently maximize the joint posterior probability of all the node labels by a simple greedy algorithm. Moreover, this can be further extended to the minimization of the expected symmetric loss. Experiments are performed on a number of real-world data sets with tree- and DAG-structured label hierarchies. The proposed method consistently outperforms other hierarchical and flat multilabel classification methods.


A Hybrid Global-Local Approach for Hierarchical Classification

AAAI Conferences

Hierarchical classification is a variant of multidimensional classification where the classes are arranged in a hierarchy and the objective is to predict a class, or set of classes, according to a taxonomy. Different alternatives have been proposed for hierarchical classification, including local and global approaches. Local approaches are prone to suffer the inconsistency problem, while the global approaches tend to produce more complex models. In this paper, we propose a hybrid globallocal approach inspired on multidimensional classification. It starts by building a local multi-class classifier per each parent node in the hierarchy. In the classification phase all the local classifiers are applied simultaneously to each instance resulting in a most probable class for each classifier. A set of consistent classes are obtained, according to the hierarchy, based on three novel alternatives. The proposed method was tested on three different hierarchical classification data sets and was compared against state-of-the-art methods, resulting in significantly superior performance to the traditional topdown techniques; with competitive results against more complex top-down classifier selection methods.



Top-k Hierarchical Classification

AAAI Conferences

This paper studies a top-k hierarchical classification problem. In top-k classification, one is allowed to make k predictions and no penalty is incurred if at least one of k predictions is correct. In hierarchical classification, classes form a structured hierarchy, and misclassification costs depend on the relation between the correct class and the incorrect class in the hierarchy. Despite that the fact that both top-k classification and hierarchical classification have gained increasing interests, the two problems have always been studied separately. In this paper, we define a top-k hierarchical loss function using a real world application. We provide the Bayes-optimal solution that minimizes the expected top-k hierarchical misclassification cost. Via numerical experiments, we show that our solution outperforms two baseline methods that address only one of the two issues.