Abstract
In this paper, the problem of classification of imbalanced text data is addressed. Initially, imbalanceness present across the classes is reduced by converting each class into multiple smaller subclasses. Further, each document is represented in a lower-dimensional space of size equal to the number of subclasses using term-class relevance (TCR) measure-based transformation technique. Then, each subclass is represented in the form of an interval-valued feature vector to achieve the compactness and stored in a knowledgebase. A symbolic classifier has been effectively used for the classification of unlabeled text documents. Experiments are conducted on Reuters-21578 and TDT2 text datasets. The results reveal that the performance of the proposed method is better than the other existing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aghdam MH, Aghaee NG, Basiri ME (2009) Text feature selection using ant colony optimization. Expert Syst Appl 36(3):6843–6853
Azam N, Yao J (2012) Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Syst Appl 39:4760–4768
Bharti KK, Singh PK (2015) Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering. Expert Syst Appl 42:3105–3114
Elhadad MK, Khaled M, Badran KM, Salama G (2017) A novel approach for ontology-based dimensionality reduction for web text document classification. In: International conference on information systems (ICIS)-2017, vol 978. IEEE, pp 5090–5507
Guru DS, Harish BS, Manjunath S (2010) Symbolic representation of text documents. In: Proceedings of the third annual ACM Bangalore conference (COMPUTE ‘10). ACM, New York, NY, USA, Article 18, 4 pp.
Guru DS, Nagendraswamy HS (2006) Symbolic representation of two-dimensional shapes. Pattern Recognit Lett 28:144–155
Guru DS, Prakash HN (2009) Online signature verification and recognition: an approach based on symbolic representation. IEEE TPAMI 31(6):1059–1073
Guru DS, Suhil M (2015) A novel term class relevance measure for text categorization. Procedia Comput Sci 45:13–22
Harish BS, Guru DS, Manjunath S (2010) Representation and classification of text documents: a brief review. IJCA Spec Issue on RTIPPR 110–119
Isa D, Lee LH, Kallimani VP, Rajkumar R (2008) Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE TKDE 20:1264–1272
Junejo KA, Karim A, Tahir MH, Jeon M (2016) Terms-based discriminative Information space for robust text classification. Inf Sci 372:518–538
Lavanya NR, Suhil M, Guru DS, Harsha SG (2016) Cluster based symbolic representation for skewed text categorization. In: International conference on recent trends in image processing and pattern recognition (RTIP2R)-2016, vol 709. Springer-CCIS, pp 202–216
Meng J, Lin H, Yu Y (2011) A two-stage feature selection method for text categorization. Comput Math Appl 62(7):2793–2800
Pinheiro RHW, Cavalcanti GDC, Ren TI (2015) Data-driven global-ranking local feature selection methods for text categorization. Expert Syst Appl 42:1941–1949
Pinheiro RHW, Cavalcanti GDC, Correa RF, Ren TI (2012) A global-ranking local feature selection method for text categorization. Expert Syst Appl 39:12851–12857
Punitha P, Guru DS (2008) Symbolic image indexing and retrieval by spatial similarity: an approach based on B-tree. Pattern Recognit 41(6):2068–2085
Rehman A, Javed K, Babri HA (2017) Feature selection based on a normalized difference measure for text classification. Inf Process Manag 53:473–489
Rehman A, Javed K, Babri HA, Saeed M (2015) Relative discrimination criterion—a novel feature ranking method for text data. Expert Syst Appl 42:3670–3681
Sabbaha T, Selamat A, Selamat MH, Fawaz S, Viedmae AEH, Krejcarg O (2017) Modified frequency-based term weighting schemes for text classification. Appl Soft Comput 58:193–206
Suhil M, Guru DS, Lavanya NR, Harsha SG (2016) Simple yet effective classification model for skewed text categorization. In: International conference on computing, communications and informatics (ICACCI)-2016. IEEE, pp 904–910
Uysal AK (2016) An improved global feature selection scheme for text classification. Expert Syst Appl 43:82–92
Uysal AK, Gunal S (2012) A novel probabilistic feature selection method for text classification. Knowl-Based Syst 36:226–235
Vieira AS, Borrajo L, Iglesias EL (2016) Improving the text classification using clustering and a novel HMM to reduce the dimensionality. Comput Methods Programs Biomed 136:119–130
Wang D, Zhang H, Li R, Lv W, Wang D (2014) t-Test feature selection approach based on term frequency for text categorization. Pattern Recognit Lett 45:1–10
Yang J, Liu Y, Zhu X, Liu Z, Zhang X (2012) A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization. Inf Process Manag 48:741–754
Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Proceedings of the 14th international conference on machine learning, vol 97, pp 412–420
Zeina D, Al-Anzi FS (2017) Employing fisher discriminant analysis for Arabic text classification. Comput Electr Eng 000:1–13
Zhang L, Jiang L, Li C, Kong G (2016) Two feature weighting approaches for naive Bayes text classifiers. Knowl-Based Syst 100(c):137–144
Zong W, Wu F, Chu LK, Sculli D (2015) A discriminative and semantic feature selection method for text categorization. Int J Prod Econ 165:215–222
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swarnalatha, K., Guru, D.S., Anami, B.S., Suhil, M. (2019). Classwise Clustering for Classification of Imbalanced Text Data. In: Sridhar, V., Padma, M., Rao, K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802-9_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-5802-9_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5801-2
Online ISBN: 978-981-13-5802-9
eBook Packages: EngineeringEngineering (R0)