Multi-labeled Data Expressed by a Set of Labels

Collected data must be organized to be utilized efficiently, and hierarchical classification of data is efficient approach to organize data. When data is classified to multiple categories or annotated with a set of labels, users request multi-labeled data by giving a set of labels. There are several interpretations of the data expressed by a set of labels. This paper discusses which data is expressed by a set of labels by introducing orders for sets of labels and shows that there are four types of orders, which are characterized by whether the labels of expressed data includes every label of the given set of labels within the range of the set. Desirable properties of the orders, data is also expressed by the higher set of labels and different sets of labels express different data, are discussed for the orders.

• Tetsuya Furukawa
• Masahiro Kuzunishi

• Classification Hierarchies
• Multi-labeled Data
• Multiple Classificaiton
• Orders of Sets of Labels

[1] G. Adami, P. Avesani, and D. Sona, "Bootstrapping for Hierarchical
Document Classification," Proc. Int-l Conf. on on Information and
Knowledge Management (CIKM-03), pp. 295-302, 2003.
[2] E. Bertino, J. Fan, E. Ferrari, M. Hachi, and A. Elamagarmid, "A
Hierarchical Access Control Model for Video Database Systems," ACM
Transactions on Information Systems, Vol.21, No.2, pp. 151-191, 2003.
[3] K. Chakrabarti, V. Ganti, J. Han, and D. Xin, "Rankig Objects by
Exploiting, Relationships: Computing Top-K over Aggregation," Proc.
ACM SIGMOD Int-l Conf. on Management of Data, pp. 371-382, 2006.
[4] S. Chuang and L. Chien, "Taxonomy Generation for Text Segments:A
Practical Web-Based Approach," ACM Transactions on Information
Systems, Vol.23, No.4, pp. 363-396, 2005.
[5] W. Dakka, P. G. Ipeirotis, and K. R. Wood, "Automatic Construction of
Multifaceted Browsing Interfaces," Proc. Int-l Conf. on Information and
Knowledge Management (CIKM-05), pp. 768-775, 2005.
[6] S. Dumais and H. Chen, "Hierarchical Classification of Web Content,"
Proc. ACM Int-l Conf. on Research and Development in Information
Retrieval, pp. 256-263, 2000.
[7] T. Furukawa and M. Kuzunishi, "Hierarchical Classification of Heterogeneous
Data," Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2005), pp. 252-257, 2005.
[8] N. Ghamrawi and A. McMallum, "Collecticve Multi-Label Classification,"
Proc. Int-l Conf. on Information and Knowledge Management
(CIKM-05), pp. 195-200, 2005.
[9] D. Koller and M. Sahami, "Hierarchically Classifying Documents Using
Very Few Words," Proc. Int-l Conf. on Machine Learning, pp. 170-178,
1997.
[10] M. Kuzunishi and T. Furukawa, "Representation for Multiple Classified
Data," Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2006), pp. 135-142, 2006.
[11] A. Sun and E. Lim, "Hierarchical Text Classification and Evaluation,"
Proc. IEEE Int-l Conf. on Data Mining (ICDM2001), pp. 521-528, 2001.
[12] K. Toutanova, F. Chen, K. Popat, and T. Hofmann, "Text Classification in
a Hierarchical Mixture Model for Small Training Sets," Proc. Int-l Conf.
on Information and Knowledge Management (CIKM-01), pp. 105-112,
2001.
[13] K. Wang, S. Zhou, and Y. He, "Hierarchical Classification of Real Life
Documents," Proc. SIAM Int-l Conf. on Data Mining, pp. 1-16, 2001.
[14] K. Wang, S. Zhou, and S. C. Liew, "Building Hierarchical Classifiers
Using Class Proximity," Proc. Int-l Conf. on Very Large Data Bases
(VLDB-99), pp. 363-374, 1999.