Abstract
We examine supervised learning for multi-class, multi-label text classification. We are interested in exploring classification in a real-world setting, where the distribution of labels may change dynamically over time. First, we compare the performance of an array of binary classifiers trained on the label distribution found in the original corpus against classifiers trained on balanced data, where we try to make the label distribution as nearly uniform as possible. We discuss the performance trade-offs between balanced vs. unbalanced training, and highlight the advantages of balancing the training set. Second, we compare the performance of two classifiers, Naive Bayes and SVM, with several feature-selection methods, using balanced training. We combine a Named-Entity-based rote classifier with the statistical classifiers to obtain better performance than either method alone.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Henceforth we use the terms label, class and (industry) sector interchangeably.
- 3.
The commonly-used pre-processed data from [14] is not suitable, for two reasons: (a) we need plain text as input for IE, and (b) the preprocessed dataset contains only unigrams, while we use a combination of unigrams and bigrams as features.
- 4.
For example, we merge I64000 and I65000, both called Retail Distribution.
- 5.
Otherwise we cannot guarantee that each sector will have a sufficient number of instances in the training and test pools. For example, if we collect the training and testing data in random order and happen to start with the largest sectors, then by the time we come to the smallest sectors all of its data may already be included in the training pool (due to multiple labeling of documents), leaving none for testing.
References
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)
Cisse, M.M., Usunier, N., Arti, T., Gallinari, P.: Robust Bloom filters for large multilabel classification tasks. In: Advances in Neural Information Processing Systems, pp. 1851–1859 (2013)
Dendamrongvit, S., Kubat, M.: Undersampling approach for imbalanced training sets and induction from multi-label text-categorization domains. In: Theeramunkong, T., Nattee, C., Adeodato, P.J.L., Chawla, N., Christen, P., Lenca, P., Poon, J., Williams, G. (eds.) New Frontiers in Applied Data Mining. LNCS, vol. 5669, pp. 40–52. Springer, Heidelberg (2010)
Dhondt, E., Verberne, S., Weber, N., Koster, C., Boves, L.: Using skipgrams and pos-based feature selection for patent classification. Comput. Linguist. Neth. 2, 52–70 (2012)
Erenel, Z., Altınçay, H.: Improving the precision-recall trade-off in undersampling-based binary text categorization using unanimity rule. Neural Comput. Appl. 22(1), 83–100 (2013)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Huang, R., Riloff, E.: Classifying message board posts with an extracted lexicon of patient attributes. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1557–1562 (2013)
Huttunen, S., Vihavainen, A., Du, M., Yangarber, R.: Predicting relevance of event extraction for the end user. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds.) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing, pp. 163–176. Springer, Berlin (2012)
Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429–449 (2002)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. Technical report 1997–75, Stanford InfoLab, February 1997
Kotsiantis, S., Kanellopoulos, D., Pintelas, P., et al.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30(1), 25–36 (2006)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Liu, Y., Loh, H.T., Sun, A.: Imbalanced text classification: a term weighting approach. Expert Syst. Appl. 36(1), 690–701 (2009)
Prati, R.C., Batista, G.E.A.P.A., Monard, M.C.: Class imbalances versus class overlapping: an analysis of a learning system behavior. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 312–321. Springer, Heidelberg (2004)
Puurula, A.: Scalable text classification with sparse generative modeling. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS, vol. 7458, pp. 458–469. Springer, Heidelberg (2012)
Stamatatos, E.: Author identification: using text sampling to handle the class imbalance problem. Inf. Process. Manage. 44(2), 790–799 (2008)
Tikk, D., Biró, G.: Experiments with multi-label text classifier on the Reuters collection. In: Proceedings of the International Conference on Computational Cybernetics (ICCC 03), pp. 33–38 (2003)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. (IJDWM) 3(3), 1–13 (2007)
Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retrieval 1(1–2), 69–90 (1999)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, vol. 97, pp. 412–420 (1997)
Zhang, W., Yoshida, T., Tang, X.: A comparative study of TF*IDF, LSI and multi-words for text classification. Expert Syst. Appl. 38(3), 2758–2765 (2011)
Zhuang, D., Zhang, B., Yang, Q., Yan, J., Chen, Z., Chen, Y.: Efficient text classification by weighted proximal SVM. In: Fifth IEEE International Conference on Data Mining (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Du, M., Pierce, M., Pivovarova, L., Yangarber, R. (2014). Supervised Classification Using Balanced Training. In: Besacier, L., Dediu, AH., MartÃn-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2014. Lecture Notes in Computer Science(), vol 8791. Springer, Cham. https://doi.org/10.1007/978-3-319-11397-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-11397-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11396-8
Online ISBN: 978-3-319-11397-5
eBook Packages: Computer ScienceComputer Science (R0)