Classifying Bio-Chip Data using an Ant Colony System Algorithm
Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.
[1] Barbara Comes, Arpad Kelemen. Probabilistic neural network
classification for microarraydata. IEEE, 2003.
[2] J. Bala, J. Huang , H. Vafaiem K. DeJong and H. Wechsler. Hybrid
Learning Using Genetic Algorithms and Decision Trees for Pattern
Classification. IJCAI conference, 1995.
[3] Jiawei Han, Micheline Kamber. Data Mining Concepts and Techniques.
Morgan Kaufmann, 2001.
[4] Lizhuang Zhao, Mohammed J. Zaki, TriCluster: An Effecitive Algorithm
for Mining Coferent Clusters in 3D Microarray Data. SIGMOD ,
Baltimore, Maryland, USA, June(2005).
[5] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. The Ant System:
Optimization by a colony of cooperating agents. IEEE Transactions on
Systems, Vol.26, No.1, 1996.
[6] Marco Dorigo, and Luca Maria Gambardella. Ant colonies for the
traveling salesman problem. BioSystems, 1997.
[7] Nicholas Holden and Alex A. Freitas. Web Page Classification with an
Ant Colony Algorithm. Parallel Problem Solving from Nature - PPSN
VIII, LNCS 3242, pages 1092-1102. Springer-Verlag, September 2004.
[8] Sorin Draghici. Data Analysis Tools for DNA Microarrays. Chapman &
Hall, 2003.
[9] Wikipedia, http://www.wikipedia.org/
[10] Yi-Shiou Chen and Tah-Hsiung Chu, A Neural Network Classification
Tree, IEEE, 1995.
[1] Barbara Comes, Arpad Kelemen. Probabilistic neural network
classification for microarraydata. IEEE, 2003.
[2] J. Bala, J. Huang , H. Vafaiem K. DeJong and H. Wechsler. Hybrid
Learning Using Genetic Algorithms and Decision Trees for Pattern
Classification. IJCAI conference, 1995.
[3] Jiawei Han, Micheline Kamber. Data Mining Concepts and Techniques.
Morgan Kaufmann, 2001.
[4] Lizhuang Zhao, Mohammed J. Zaki, TriCluster: An Effecitive Algorithm
for Mining Coferent Clusters in 3D Microarray Data. SIGMOD ,
Baltimore, Maryland, USA, June(2005).
[5] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. The Ant System:
Optimization by a colony of cooperating agents. IEEE Transactions on
Systems, Vol.26, No.1, 1996.
[6] Marco Dorigo, and Luca Maria Gambardella. Ant colonies for the
traveling salesman problem. BioSystems, 1997.
[7] Nicholas Holden and Alex A. Freitas. Web Page Classification with an
Ant Colony Algorithm. Parallel Problem Solving from Nature - PPSN
VIII, LNCS 3242, pages 1092-1102. Springer-Verlag, September 2004.
[8] Sorin Draghici. Data Analysis Tools for DNA Microarrays. Chapman &
Hall, 2003.
[9] Wikipedia, http://www.wikipedia.org/
[10] Yi-Shiou Chen and Tah-Hsiung Chu, A Neural Network Classification
Tree, IEEE, 1995.
@article{"International Journal of Information, Control and Computer Sciences:52605", author = "Minsoo Lee and Yearn Jeong Kim and Yun-mi Kim and Sujeung Cheong and Sookyung Song", title = "Classifying Bio-Chip Data using an Ant Colony System Algorithm", abstract = "Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.", keywords = "Ant Colony System, DNA chip data, Classification.", volume = "2", number = "8", pages = "2608-5", }