Cancer detection is one of the key research topics in the medical field.
Accurate detection of different cancer types is valuable in providing better
treatment facilities and risk minimization for patients. This paper deals with
the classification problem of human cancer diseases by using gene expression
data. It is presented a new methodology to analyze microarray datasets and
efficiently classify cancer diseases. The new method first employs Signal to
Noise Ratio (SNR) to find a list of a small subset of non-redundant genes.
Then, after normalization, it is used Multi-Objective Particle Swarm
Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest
Neighborhood (KNN) for cancer disease classification. This method improves the
classification accuracy of cancer classification by reducing the number of
features. The proposed methodology is evaluated by classifying cancer diseases
in five cancer datasets. The results are compared with the most recent
approaches, which increases the classification accuracy in each dataset.

本研究提出了一种新的方法，使用基因表达数据进行癌症分类问题的解决，首先运用信噪比算法筛选出非冗余基因的子集，然后运用多目标粒子群算法进行特征选择，并采用自适应 K 最近邻算法进行癌症分类，从而提高了癌症分类的准确性。

基于自适应 K 近邻的多目标 PSO 微阵列表达数据基因选择

Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood

In high-dimensional prediction problems, where the number of features may
greatly exceed the number of training instances, fully Bayesian approach with a
sparsifying prior is known to produce good results but is computationally
challenging. To alleviate this computational burden, we propose to use a
preprocessing step where we first apply a dimension reduction to the original
data to reduce the number of features to something that is computationally
conveniently handled by Bayesian methods. To do this, we propose a new
dimension reduction technique, called iterative supervised principal components
(ISPC), which combines variable screening and dimension reduction and can be
considered as an extension to the existing technique of supervised principal
components (SPCs). Our empirical evaluations confirm that, although not
foolproof, the proposed approach provides very good results on several
microarray benchmark datasets with very affordable computation time, and can
also be very useful for visualizing high-dimensional data.

为了解决高维预测问题的计算难题，提出了一种基于 Bayesian 方法和新的维度缩减技术 ISPC 的预处理方法，在多个微阵列基准数据集上得到了很好的结果，并可用于高维数据可视化。