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Classification Using Flexible Neural Tree

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The purpose of my work is to develop 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.. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a emetic 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. The results show that proposed method is efficient for both input feature selection and improved classification rate.

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