Learning Classifiers Using Hierarchically Structured Class Taxonomies

Wu, Feihong; Zhang, Jun; Honavar, Vasant

doi:10.1007/11527862_24

Cited by 66 publications

(41 citation statements)

References 4 publications

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“…By doing this, the class predictions respect the hierarchy constraints. This approach was proposed by Wu et al (2005) and was referred to as "Binarized Structured Label Learning" (BSLL).…”

Section: Local Classifier Per Node Approachmentioning

confidence: 99%

“…There are, at least, two approaches that could be used to cope with the multi-label scenario. One is to use a multi-label classifier at each parent node, as done by Wu et al (2005). The second approach is to take into account the different confidence scores provided by each classifier and have some kind of decision thresholds based on those scores to allow multiple labels.…”

Section: Local Classifier Per Parent Node Approachmentioning

confidence: 99%

“…Wu et al (2005) have defined a class taxonomy as a tree structured regular concept hierarchy defined over a partially order set (C, ≺), where C is a finite set that enumerates all class concepts in the application domain, and the relation ≺ represents the "IS-A" relationship. Wu et al (2005) define the "IS-A" relationship as both anti-reflexive and transitive. However, we prefer to define the "IS-A" relationship as asymmetric, anti-reflexive and transitive:…”

Section: What Is Hierarchical Classification?mentioning

confidence: 99%

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A survey of hierarchical classification across different application domains

Silla

Freitas

2010

Data Min Knowl Disc

831

539

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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.

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“…By doing this, the class predictions respect the hierarchy constraints. This approach was proposed by Wu et al (2005) and was referred to as "Binarized Structured Label Learning" (BSLL).…”

Section: Local Classifier Per Node Approachmentioning

confidence: 99%

Section: Local Classifier Per Parent Node Approachmentioning

confidence: 99%

Section: What Is Hierarchical Classification?mentioning

confidence: 99%

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A survey of hierarchical classification across different application domains

Silla

Freitas

2010

Data Min Knowl Disc

831

539

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“…The first proposals in which sequential boolean decisions are applied in combination with local classifiers per node can be found in (D'Alessio et al, 2000), (Dumais & Chen, 2000), and (Sun & Lim, 2001). In Wu et al (2005), the idea of mirroring the taxonomy structure through binary classifiers is clearly highlighted; the authors call this technique "binarized structured label learning". In PF, given a taxonomy, where each node represents a classifier entrusted with recognizing all corresponding positive inputs (i.e., interesting documents), each input traverses the taxonomy as a "token", starting from the root.…”

Section: Vertical Combinationmentioning

confidence: 99%

Retrieving and Categorizing Bioinformatics Publications through a MultiAgent System

Addis¹,

Armano²,

Vargiu³

et al. 2011

Computational Biology and Applied Bioinformatics

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In this chapter, we present PUB.MAS, a multiagent system able to retrieve and categorize bioinformatics publications from selected Web sources. The chapter extends and revises our previous work (Armano et al., 2007). The main extensions consist of a more detailed presentation of the information extraction task, a deep explanation of the adopted hierarchical text categorization technique, and the description of the prototype that has been implemented. Built upon X.MAS (Addis et al., 2008), a generic multiagent architecture aimed at retrieving, filtering and reorganizing information according to user interests, PUB.MAS is able to: (i) extract information from online digital archives; (ii) categorize publications according to a given taxonomy; and (iii) process user's feedback. As for information extraction, PUB.MAS provides specific wrappers able to extract publications from RSS-based Web pages and from Web Services. As for categorization, PUB.MAS performs Progressive Filtering (PF), the effective hierarchical text categorization technique described in (Addis et al., 2010). In its simplest setting, PF decomposes a given rooted taxonomy into pipelines, one for each existing path between the root and each node of the taxonomy, so that each pipeline can be tuned in isolation. To this end, a threshold selection algorithm has been devised, aimed at finding a sub-optimal combination of thresholds for each pipeline. PUB.MAS provides also suitable strategies to allow users to express what they are really interested in and to personalize search results accordingly. Moreover, PUB.MAS provides a straightforward approach to user feedback with the goal of improving the performance of the system depending on user needs and preferences. The prototype allows users to set the sources from which publications will be extracted and the topics s/he is interested in. As for the digital archives, the user can choose between BMC Bioinformatics and PubMed Central. As for the topics of interest, the user can select one or more categories from the adopted taxonomy, which is taken from the TAMBIS ontology (Baker et al., 1999). The overall task begins with agents able to handle the selected digital archives, which extract the candidate publications. Then, all agents that embody a classifier trained on the selected topics are involved to perform text categorization. Finally, the system supplies the user with the selected publications through suitable interface agents. The chapter is organized as follows. First, we give a brief survey of relevant related work on: (i) scientific publication retrieval; (ii) hierarchical text categorization; and (iii) multiagent systems in information retrieval. Subsequently, we concentrate on the task of retrieving and categorizing bioinformatics publications. Then, PUB.MAS is illustrated together with its performances and the implemented prototype. Conclusions end the chapter.

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“…Desse modo, a taxonomia de classes pode ser definida como uma árvore estruturada sobre um conjunto parcialmente ordenado (L, ≺), onde L é um conjunto finito composto por todas as classes presentes na taxonomia e ≺ representa a relação é uma (is-a) assimétrica, antirreflexiva e transitiva (Silla Jr & Freitas, 2011;Wu et al, 2005). Essas propriedades são definidas a seguir:…”

Section: Fundamentos E Definições Para Classificação Hierárquicaunclassified

Abordagens para aprendizado semissupervisionado multirrótulo e hierárquico

Metz¹

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Learning Classifiers Using Hierarchically Structured Class Taxonomies

Cited by 66 publications

References 4 publications

A survey of hierarchical classification across different application domains

A survey of hierarchical classification across different application domains

Retrieving and Categorizing Bioinformatics Publications through a MultiAgent System

Abordagens para aprendizado semissupervisionado multirrótulo e hierárquico

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