Attribute Selection Methods Comparison for Classification of Diffuse Large B-Cell Lymphoma
The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.
[1] Alberts, B. et al. Biologia Molecular da Célula. Editora Artes Médicas,
3ª Edição, 1997.
[2] Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma
identified by gene expression profiling. Nature 4051, 503-511 (2000).
[3] Bala, J.; Jong K. De; Huang, J.; Vafaie, H.; Wechsler, H; Using Learnig
to Facilite the Evolution of Features for Recognizing Visual Concepts,
In: Special Issue of Evolutionary Computatuion - Evolution, learning
and Instinct: 100 years of Baldwin Effect, Vol. 4 , pp. 297-311. 1996.
[4] Billroth, T., Multiple Lymphoma: Erfolgreiche Behandlung mit Arsenik,
Deutsch Med. Wschr, Stuttgart, V. 21, 1066-1067, 1871.
[5] Boz, O., Feature Subset Selection by Using Sorted Feature Relevance,
In: ICMLA 2002 - International Conference on Machine Learning and
Applications, USA, 2002.
[6] Freitas, A. A.; Understanding the Crucial Role of Attributes Interaction
in Data Mining, In: Artificial Intelligence Review 16, pp 177-199,
Kluwer Academic Publishers, 2001.
[7] Hodgkin, T., On Some Morbid Appearances of the Absorbant Glands
and Spleen, Med.-Chir. Trans., 17, 68-114, 1832.
[8] Holsheimer, M.; Siebes, A., Data Minig - The Search for Knowledge in
Databases, Report CS-R9406, Amsterdam, 1991.
[9] Kohavi, R.; John, G. H., The Wrapper Approach, In: H. Liu & H.
Motoda (Eds.) Feature Extraction, Construction and Selection: a data
mining perspective, 33-49. Kluwer, 1998.
[10] Liu, H., Motoda, H., Feature Selection for Knowledge Discovery and
Data Minig, Kluwer academic Publishers, 1998.
[11] Liu, H., Motoda, H., Yu, L., The Handbook of Data Mining, Lawrence
Erlbaum Associates, Inc. Publishers. Editor: N. Ye. PP 409 - 423. 2003.
[12] Molina L. C., Belanche L., Nebot A. Feature Selection Algorithms: A
Survey and experimental Evaluation. Technical Report LSI-02-62-R
Universitat Politècnica de Catalunya, Barcelona, Spain, 2002.
[13] Shipp, M.A. et al. Diffuse large B-cell lymphoma outcome prediction by
gene expression profiling and supervised machine learning. Nature, Vol.
8, N. 1, 68-74, 2002.
[1] Alberts, B. et al. Biologia Molecular da Célula. Editora Artes Médicas,
3ª Edição, 1997.
[2] Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma
identified by gene expression profiling. Nature 4051, 503-511 (2000).
[3] Bala, J.; Jong K. De; Huang, J.; Vafaie, H.; Wechsler, H; Using Learnig
to Facilite the Evolution of Features for Recognizing Visual Concepts,
In: Special Issue of Evolutionary Computatuion - Evolution, learning
and Instinct: 100 years of Baldwin Effect, Vol. 4 , pp. 297-311. 1996.
[4] Billroth, T., Multiple Lymphoma: Erfolgreiche Behandlung mit Arsenik,
Deutsch Med. Wschr, Stuttgart, V. 21, 1066-1067, 1871.
[5] Boz, O., Feature Subset Selection by Using Sorted Feature Relevance,
In: ICMLA 2002 - International Conference on Machine Learning and
Applications, USA, 2002.
[6] Freitas, A. A.; Understanding the Crucial Role of Attributes Interaction
in Data Mining, In: Artificial Intelligence Review 16, pp 177-199,
Kluwer Academic Publishers, 2001.
[7] Hodgkin, T., On Some Morbid Appearances of the Absorbant Glands
and Spleen, Med.-Chir. Trans., 17, 68-114, 1832.
[8] Holsheimer, M.; Siebes, A., Data Minig - The Search for Knowledge in
Databases, Report CS-R9406, Amsterdam, 1991.
[9] Kohavi, R.; John, G. H., The Wrapper Approach, In: H. Liu & H.
Motoda (Eds.) Feature Extraction, Construction and Selection: a data
mining perspective, 33-49. Kluwer, 1998.
[10] Liu, H., Motoda, H., Feature Selection for Knowledge Discovery and
Data Minig, Kluwer academic Publishers, 1998.
[11] Liu, H., Motoda, H., Yu, L., The Handbook of Data Mining, Lawrence
Erlbaum Associates, Inc. Publishers. Editor: N. Ye. PP 409 - 423. 2003.
[12] Molina L. C., Belanche L., Nebot A. Feature Selection Algorithms: A
Survey and experimental Evaluation. Technical Report LSI-02-62-R
Universitat Politècnica de Catalunya, Barcelona, Spain, 2002.
[13] Shipp, M.A. et al. Diffuse large B-cell lymphoma outcome prediction by
gene expression profiling and supervised machine learning. Nature, Vol.
8, N. 1, 68-74, 2002.
@article{"International Journal of Medical, Medicine and Health Sciences:64783", author = "Helyane Bronoski Borges and Júlio Cesar Nievola", title = "Attribute Selection Methods Comparison for Classification of Diffuse Large B-Cell Lymphoma", abstract = "The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.", keywords = "Attribute selection, data mining.", volume = "1", number = "8", pages = "502-5", }