On the suitability of combining feature selection and resampling to manage data complexity

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5988 LNAI, Issue , 2010, Pages 141-150

  Martín Félez, Raúl ^a   Mollineda, Ramón A ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

class imbalance problem; Data complexity; feature reduction; melody finding; music information retrieval

Indexed keywords

BALANCING TECHNIQUES; CLASS IMBALANCE; CLASS IMBALANCE PROBLEMS; CLASSIFICATION RESULTS; CORRELATION STUDIES; DATA COMPLEXITY; FEATURE REDUCTION; FEATURE SELECTION; LEARNING TASKS; MIDI FILES; MUSIC INFORMATION RETRIEVAL; PREPROCESSING TECHNIQUES; RESAMPLING; SPECIFIC PROBLEMS; TRAINING SETS;

ARTIFICIAL INTELLIGENCE; ELECTRONIC MUSICAL INSTRUMENTS; FEATURE EXTRACTION; INFORMATION RETRIEVAL;

DATA REDUCTION;

EID: 77955046700     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14264-2_15     Document Type: Conference Paper

References (11)

1. Basu, M., Ho, T.: Data Complexity in Pattern Recognition. Springer, New York (2006)
2. Garća, V., Alejo, R., Śanchez, J.S., Sotoca, J.M., Mollineda, R.A.: Combined effects of class imbalance and class overlap on instance-based classification. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol.4224, pp. 371-378. Springer, Heidelberg (2006)
3. Zheng, Z., Wu, X., Srihari, R.: Feature selection for text categorization on imbalanced data. SIGKDD Explor. Newsl. 6(1), 80-89 (2004)
4. Al-shahib, A., Breitling, R., Gilbert, D.: Feature selection and the class imbalance problem in predicting protein function from sequence. Applied Bioinf. 4 (2005)
5. Mart́n, R., Mollineda, R., Garća, V.: Melodic track identification in midi files considering the imbalanced context. In: 4th IbPRIA, Ṕovoa de Varzim (2009)
6. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. J. Artif. Intell. Res (JAIR) 16, 321-357 (2002)
7. Kotsiantis, S.: Mixture of expert agents for handling imbalanced data sets. Annals of Mathematics, Computing & TeleInformatics 1, 46-55 (2003)
8. Hall, M.: Correlation-based feature subset selection for machine learning (1999)
9. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (2002)
10. Provost, F., Fawcett, T.: Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In: Proc. of the 3rd ACM SIGKDD, pp. 43-48 (1997)
11. Rizo, D., Ponce de Léon, P., Ṕerez-Sancho, C., Pertusa, A., Iñesta, J.: A pattern recognition approach for melody track selection in midi files. In: Proc. of the 7th ISMIR, Victoria, Canada, pp. 61-66 (2006)