Quinzán Ianisse, Sotoca José M, Latorre-Carmona Pedro, Pla Filiberto, García-Sevilla Pedro, Boldó Enrique
Institute of New Imaging Technologies, Jaume I University, Castellón, Spain.
Biomed Opt Express. 2013 Apr 1;4(4):514-9. doi: 10.1364/BOE.4.000514. Epub 2013 Mar 4.
A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.
一种由合成少数类过采样技术（SMOTE）和顺序向前浮动选择（SFFS）技术相结合的方法，被用于在一个高度不平衡、小尺寸的黑色素瘤和非黑色素瘤病变的两类多光谱数据集中进行波段选择。目的是提高分类率，并有助于识别那些在黑色素瘤检测中起更重要作用的光谱波段。所有处理步骤的设计都考虑到了数据集中样本数量较少的情况，这种情况在医疗案例中相当常见。训练/测试集采用留一法策略构建。应用SMOTE来处理不平衡问题，并结合合格多数投票方案（QMV）。支持向量机（SVM）是应用于每个平衡集的分类方法。结果表明，所有黑色素瘤病变都被正确分类，使用的波段数量较少，在考虑总共55个光谱波段中的9个波段时，灵敏度达到100%，特异性达到72%。
