In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with l...

The design, implementation, and evaluation of a system for automatic audio signal classification is presented. The signals are classified according to audio type, differentiating between three speech classes, 13 musical genres, and background noise. A large number of audio features are evaluated for their suitability in such a classification task, including MPEG-7 descriptors and several new fe...

In this paper, we systematically study the problem of dataless hierarchical text classification. Unlike standard text classification schemes that rely on supervised training, dataless classification depends on understanding the labels of the sought after categories and requires no labeled data. Given a collection of text documents and a set of labels, we show that understanding the labels can b...

Naive Bayes models have been very popular in several classification tasks. In this paper we study the application of these models to classification tasks where the data is sparse i.e., a large number of possible outcomes do not appear in the data. Traditionally point estimates of the model parameters and in particular, point estimates based on the Laplace’s rule have been popular for such spars...

Speech emotion as anger, boredom, fear, gladness, etc. is high semantic information and its automatic analysis may have many applications such as smart human-computer interactions or multimedia indexing. Main difficulties for an efficient speech emotion classification reside in complex emotional class borders leading to necessity of appropriate audio feature selection. While current work in the...

Hierarchical Multi-Label Classification is a complex classification problem where the classes are hierarchically structured. This task is very common in protein function prediction, where each protein can have more than one function, which in turn can have more than one sub-function. In this paper, we propose a novel hierarchical multi-label classification algorithm for protein function predict...

Hierarchical Classification refers to assigning of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose a topdown level-based classification method that can classify documents to both leaf and internal categories. ...

In this paper, we present a comparison of three audio taxonomy methods for MPEG-7 sound classification. The MPEG-7 sound classification and indexing tools consist of both low-level and high-level description schemes. For the low-level descriptors that we use, low-dimensional features based on spectral basis descriptors are produced in three stages: normalized audio spectrum envelope, principal ...

Hierarchical classification problems are multiclass supervised learning problems with a predefined hierarchy over the set of class labels. In this work, we study the consistency of hierarchical classification algorithms with respect to a natural loss, namely the tree distance metric on the hierarchy tree of class labels, via the usage of calibrated surrogates. We first show that the Bayes optim...

Large numbers of overlapping classes are found to be organized in hierarchies in many domains. In multi-label classification over such a hierarchy, members of a class must also belong to all of its parents. Training an independent classifier for each class is a common approach, but this may yield labels for a given example that collectively violate this constraint. We propose a principled metho...