%0 Journal Article %1 Chen:2006:N %A Chen, Yuehui %A Abraham, Ajith %A Yang, Bo %D 2006 %J Neurocomputing %K Breast Flexible Intrusion Memetic algorithm, algorithms, cancer classification detection genetic model, neural programming, system, tree %N 1-3 %P 305--313 %R doi:10.1016/j.neucom.2006.01.022 %T Feature selection and classification using flexible neural tree %V 70 %X The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.

@article{Chen:2006:N, abstract = {The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chen, Yuehui and Abraham, Ajith and Yang, Bo}, biburl = {https://www.bibsonomy.org/bibtex/2481fa7add4a9d1aa63ba899298e9e866/brazovayeye}, doi = {doi:10.1016/j.neucom.2006.01.022}, interhash = {cc934855d89d333b63e7100b88e04050}, intrahash = {481fa7add4a9d1aa63ba899298e9e866}, journal = {Neurocomputing}, keywords = {Breast Flexible Intrusion Memetic algorithm, algorithms, cancer classification detection genetic model, neural programming, system, tree}, month = {December}, note = {Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN '04), 7th Brazilian Symposium on Neural Networks}, number = {1-3}, pages = {305--313}, timestamp = {2008-06-19T17:37:49.000+0200}, title = {Feature selection and classification using flexible neural tree}, volume = 70, year = 2006 }