Analyzing Multi-Labeled Data Based on the Roll of a Concept against a Semantic Range
Classifying data hierarchically is an efficient approach
to analyze data. Data is usually classified into multiple categories, or
annotated with a set of labels. To analyze multi-labeled data, such
data must be specified by giving a set of labels as a semantic range.
There are some certain purposes to analyze data. This paper shows
which multi-labeled data should be the target to be analyzed for
those purposes, and discusses the role of a label against a set of
labels by investigating the change when a label is added to the set of
labels. These discussions give the methods for the advanced analysis
of multi-labeled data, which are based on the role of a label against
a semantic range.
[1] Bertino, E., Fan, J., Ferrari, E., Hachi, M., and Elamagarmid, A.:
A Hierarchical Access Control Model for Video Database Systems,
ACM Transactions on Information Systems, Vol. 21, No. 2, pp. 151-191
(2003).
[2] Chakrabarti, K., Ganti, V., Han, J., and Xin, D.: Rankig Objects by
Exploiting, Relationships: Computing Top-K over Aggregation, Proc.
ACM SIGMOD Int-l Conf. on Management of Data, pp. 371-382 (2006).
[3] Furukawa, T. and Kuzunishi, M.: Hierarchical Classification of Heterogeneous
Data, Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2005), pp. 252-257 (2005).
[4] Furukawa, T. and Kuzunishi, M.: "Multi-labeled Data Expressed by
a Set of Labels", Proc. World Academy of Science, Engineering and
Technology, Vol. 65, pp. 857-863 (2010).
[5] Ghamrawi, N. and McMallum, A.: Collecticve Multi-Label Classification,
Proc. Int-l Conf. on Information and Knowledge Management
(CIKM-05), pp. 195-200 (2005).
[6] Kuzunishi, M. and Furukawa, T.: Representation for Multiple Classified
Data, Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2006), pp. 135-142 (2006).
[7] Silva, A. and Barbosa, D: Labeling Data Extracted from the Web, Proc.
On The Move to Meaningful Internet Systems 2007: CoopIS, DOA, and
ODBASE, pp. 1099-1116 (2007).
[8] Sun, A. and Lim, E.: Hierarchical Text Classification and Evaluation,
Proc. IEEE Int-l Conf. on Data Mining (ICDM2001), pp. 521-528
(2001).
[9] Toutanova, K., Chen, F., Popat K., and Hofmann, T.: 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).
[10] Wang, K., Zhou, S., and He, Y.: Hierarchical Classification of Real Life
Documents, Proc. SIAM Int-l Conf. on Data Mining, pp. 1-16 (2001).
[11] Wang, K., Zhou, S., and Liew, S. C.: Building Hierarchical Classifiers
Using Class Proximity, Proc. Int-l Conf. on Very Large Data Bases
(VLDB-99), pp. 363-374 (1999).
[1] Bertino, E., Fan, J., Ferrari, E., Hachi, M., and Elamagarmid, A.:
A Hierarchical Access Control Model for Video Database Systems,
ACM Transactions on Information Systems, Vol. 21, No. 2, pp. 151-191
(2003).
[2] Chakrabarti, K., Ganti, V., Han, J., and Xin, D.: Rankig Objects by
Exploiting, Relationships: Computing Top-K over Aggregation, Proc.
ACM SIGMOD Int-l Conf. on Management of Data, pp. 371-382 (2006).
[3] Furukawa, T. and Kuzunishi, M.: Hierarchical Classification of Heterogeneous
Data, Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2005), pp. 252-257 (2005).
[4] Furukawa, T. and Kuzunishi, M.: "Multi-labeled Data Expressed by
a Set of Labels", Proc. World Academy of Science, Engineering and
Technology, Vol. 65, pp. 857-863 (2010).
[5] Ghamrawi, N. and McMallum, A.: Collecticve Multi-Label Classification,
Proc. Int-l Conf. on Information and Knowledge Management
(CIKM-05), pp. 195-200 (2005).
[6] Kuzunishi, M. and Furukawa, T.: Representation for Multiple Classified
Data, Proc. IASTED Int-l Conf. on Databases and Applications
(DBA2006), pp. 135-142 (2006).
[7] Silva, A. and Barbosa, D: Labeling Data Extracted from the Web, Proc.
On The Move to Meaningful Internet Systems 2007: CoopIS, DOA, and
ODBASE, pp. 1099-1116 (2007).
[8] Sun, A. and Lim, E.: Hierarchical Text Classification and Evaluation,
Proc. IEEE Int-l Conf. on Data Mining (ICDM2001), pp. 521-528
(2001).
[9] Toutanova, K., Chen, F., Popat K., and Hofmann, T.: 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).
[10] Wang, K., Zhou, S., and He, Y.: Hierarchical Classification of Real Life
Documents, Proc. SIAM Int-l Conf. on Data Mining, pp. 1-16 (2001).
[11] Wang, K., Zhou, S., and Liew, S. C.: Building Hierarchical Classifiers
Using Class Proximity, Proc. Int-l Conf. on Very Large Data Bases
(VLDB-99), pp. 363-374 (1999).
@article{"International Journal of Information, Control and Computer Sciences:58750", author = "Masahiro Kuzunishi and Tetsuya Furukawa and Ke Lu", title = "Analyzing Multi-Labeled Data Based on the Roll of a Concept against a Semantic Range", abstract = "Classifying data hierarchically is an efficient approach
to analyze data. Data is usually classified into multiple categories, or
annotated with a set of labels. To analyze multi-labeled data, such
data must be specified by giving a set of labels as a semantic range.
There are some certain purposes to analyze data. This paper shows
which multi-labeled data should be the target to be analyzed for
those purposes, and discusses the role of a label against a set of
labels by investigating the change when a label is added to the set of
labels. These discussions give the methods for the advanced analysis
of multi-labeled data, which are based on the role of a label against
a semantic range.", keywords = "Classification Hierarchies, Data Analysis, Multilabeled Data, Orders of Sets of Labels", volume = "4", number = "10", pages = "1567-7", }