Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network

• Proposed spiking function is for the non linear integrate fire neuron model (NLIF).
• The proposed spiking function in NLIF generates biologically plausible spikes.
• Derived inter spike interval is used in the Wavelet Radial Basis Neural Network.
• SWRNN is used for the classification of benchmark gene expression datasets.
• SWRNN is superior to WRNN in terms of classification accuracy and time.

The paper discusses how Spiking Wavelet Radial Basis Neural Network can be effectively used for the classification of gene expression data. A new spiking function has been proposed in the non-linear integrate and fire model and its inter spike interval is derived and used in the Wavelet Radial Basis Neural Network for the classification of gene expression data. The proposed model is termed as Spiking Wavelet Radial Basis Neural Network (SWRNN). The classification accuracy has been evaluated on various benchmark gene expression datasets. A comparative performance evaluation of the proposed model has been made with the Wavelet Radial Basis Neural Network (WRNN) and the standard available results in terms of classification accuracy. The comparison of the proposed SWRNN and WRNN has also been done in terms of execution time. It has been observed that the increase in classification accuracy for the proposed SWRNN is highly statistically significant for most of the gene expression datasets when compared with WRNN and the standard results. Thus incorporating a spiking function in an artificial neural network can make it more powerful for classification.