Clustering Driven Cascade Classifiers for Multi-indexing of Polyphonic Music by Instruments

Jiang, Wenxin; Raś, Zbigniew W.; Wieczorkowska, Alicja

doi:10.1007/978-3-642-11674-2_2

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a multi-label problem, a pattern may belong to more than one class [14,25,35,71]. For example multi-indexing of polyphonic music by instruments and extracting emotions from music data are some interesting applications in this research area [25,35,71]. However, by referring to a multi-class classification task, we mean that each pattern belongs to only one class.…”

Section: Introductionmentioning

confidence: 99%

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

et al. 2014

View full text Add to dashboard Cite

Classification of data is an important problem which has attracted many researchers to introduce new approaches. In this paper, we propose Mapping to Optimal Regions (MOR) as a new method for multi-class classification task to reduce computational and memory complexities. It requires only one simple mapping from input space to optimal regions. The optimal domain is estimated using a multi objective cost function to increase the region size and the generalization ability of the mapping and to reduce the mapping error. Finally, the centers of optimal regions are determined with respect to the optimal size of the regions and the code assignment process which reduces the effect of inappropriate labeling. A Hierarchical version of MOR (HMOR) is presented for datasets with high number of classes or low dimensional feature spaces. By taking the advantage of MOR, the complexity reduces significantly in comparison to the other classifiers.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

et al. 2014

View full text Add to dashboard Cite

show abstract

“…and passed as an input stream to the feature extraction mechanism. Eventually, we obtained a set of elaborated features ready for multi-labeling [14].…”

Section: Classifier Training and Labelingmentioning

confidence: 99%

Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

Grzegorowski¹,

Stawicki²

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The article introduces a novel mechanism for automatic extraction of features from streams of numerical data. It was originally designed for the purpose of processing multiple streams of readings generated by sensors in coal mines. The original research was conducted on methane concentration analysis in the DISESOR project. The article demonstrates an application of the elaborated mechanism for the case of tagging short series of readings from sensors that monitor activities and movements of firefighters during the action with labels corresponding to firefighter activities. The purpose of the experiment was to assess how the automatic feature extraction and construction of classifiers (without parameters tuning and without the use of classifier ensembles) can cope with the competition's task in comparison to other participants.

show abstract

Metaheuristic Optimization of Multiple Fundamental Frequency Estimation

Rychlicki-Kicior

Stasiak

2014

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Clustering Driven Cascade Classifiers for Multi-indexing of Polyphonic Music by Instruments

Cited by 4 publications

References 6 publications

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

Metaheuristic Optimization of Multiple Fundamental Frequency Estimation

Contact Info

Product

Resources

About