Title :
Immune Network for Classifying Heterogeneous Data
Author :
Puteh, Mazidah ; Omar, Khairuddin ; Hamdan, Abdul Razak ; Bakar, Azuraliza Abu
Author_Institution :
FTMSK, Univ. Teknol. MARA, Shah Alam
Abstract :
In the previous AIS research, most of the AIS classifiers use clonal selection and require the data to be in numerical or categorical data types prior to processing. These classifiers ignore the network feature of the immune system that is suitable for classification. Furthermore, the transformation of data into any other specific types from their original form can degrade the originality of the data and consume more space and pre processing time. This paper introduces resource limited immune network model with hybrid affinity measurement for classifying heterogeneous data in its original types. The model is able to process the data with the types as represented in the database. The paper shows comparisons between the model and the selected existing immune algorithms that also uses the same set of data and parameters. The experimental results show that the immune network model produces a better accuracy rate with shorter classifier on most of the heterogeneous data from University of California, Irvive (UCI) machine learning repository (MLR).
Keywords :
artificial immune systems; data mining; pattern classification; artificial immune system; categorical data type; clonal selection; data mining; database system; heterogeneous data classification; hybrid affinity measurement; machine learning repository; numerical data type; resource-limited immune network model; Classification algorithms; Data mining; Databases; Degradation; Immune system; Intelligent networks; Intelligent systems; Machine learning; Machine learning algorithms; Pathogens; AIS; classification; heterogeneous; immune network;
Conference_Titel :
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-3382-7
DOI :
10.1109/ISDA.2008.242