With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is benef...

Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In this contribution novel classification ensemble algorithms for DAG-structured ta...

Hierarchical multi-label classification is a variant of traditional classification in which the instances can belong to several labels, that are in turn organized in a hierarchy. Existing hierarchical multi-label classification algorithms ignore possible correlations between the labels. Moreover, most of the current methods predict instance labels in a “flat” fashion without employing the ontol...

Functional classification of genes using diverse bio-molecular data obtained from high-throughput technologies is a fundamental problem in bioinformatics and functional genomics. Genes are organized and classified according to a hierarchical classification scheme and each gene will participate in multiple activities. Flat classifiers, that work on non-hierarchical classification problems indepe...

1389-1286/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.comnet.2012.04.014 ⇑ Corresponding author. Tel.: +82 2 3277 3403; fa E-mail address: [email protected] (H. Lim). Packet classification is one of the most challenging functions in Internet routers since it involves a multi-dimensional search that should be performed at wire-speed. Hierarchical packet classification is an ef...

Using image hierarchies for visual categorization has shown to have a number of important benefits. For instance it enables a significant gain in efficiency (e.g., logarithmic with the number of categories [1, 2]). Moreover, arranging visual data in a hierarchical structure echoes the way how humans organize data and enables the construction of a more meaningful distance metric for image classi...

This paper deals with categorization tasks where categories are partially ordered to form a hierarchy. First, it introduces the notion of consistent classification which takes into account the semantics of a class hierarchy. Then, it presents a novel global hierarchical approach that produces consistent classification. This algorithm with AdaBoost as the underlying learning procedure significan...

The analysis of a speech act is important for dialogue understanding systems because the speech act of an utterance is closely associated with the user’s intention in the utterance. This paper proposes a speech act classification model that effectively uses a two-layer hierarchical structure generated from the adjacency pair information of speech acts. The proposed model has two advantages when...

We present a four-step hierarchical SRL strategy which generalizes the classical two-level approach (boundary detection and classification). To achieve this, we have split the classification step by grouping together roles which share linguistic properties (e.g. Core Roles versus Adjuncts). The results show that the nonoptimized hierarchical approach is computationally more efficient than the t...

In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical learning. Our approach of "learning-follows-decomposition" is a generic solution to complex high-dimensional problems where the input space is partitioned prior to the hierarchical neural domain instead of by competitive lea...