A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search

EURASIP Journal on Advances in Signal Processing

关键词: feature selection;    dimensionality reduction;    tabu search;    1-NN classifier;    prostate cancer classification;   

DOI  :  

来源: DOAJ

【 摘 要 】

The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of a large number of spectral bands within the visible spectrum. This results in a feature vector of size greater than 100. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem. The two well-known techniques to solve this problem are feature extraction and feature selection. In this paper, a novel feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier. The experiments have been carried out on the prostate cancer textured multispectral images and the results have been compared with a reported classical feature extraction technique. The results have indicated a significant boost in the performance both in terms of minimizing features and maximizing classification accuracy.

【 授权许可】

Unknown