Classification algorithms are widely taken into account for clinical decision support systems.
However, it is not always straightforward to understand the behavior of such algorithms on a …

On-line learning in environments and applications with time-varying behavior pose serious
challenges. Changes may lead the learning model designed with old data, to become …

Metadata—the machine-readable descriptions of the data—are increasingly seen as crucial
for describing the vast array of biomedical datasets that are currently being deposited in …

Medical outcomes must be tracked in order to enhance quality initiatives, healthcare
management, and mass education. Thoracic surgery data have been acquired for those who …

In this research, intelligent classifiers for disease diagnosis are designed that use classifier
parameters, such as cost, tolerance, gamma and epsilon, with multi-objective evolutionary …

Datasets obtained from the real world are far from balanced, particularly for disease
datasets, since such datasets are usually highly skewed having a few minority classes apart …

In real world, the datasets are having varying dimensions which incorporates noisy,
irrelevant and redundant data which is hard to analyze. Feature selection is a preprocessing …

GDP 增速与CPI 指数具有复杂的时间序列和非线性特征. 鉴于BP 神经网络算法有良好的非线性
拟合能力, 但容易陷入局部极小值的特点, 提出了基于主成分分析的PSO BP 算法 …

将监控数据的已知状态作为先验类别标签, 构造出新的有监督的粒子群优化(S-PSO) 分类算法,
并对设备进行故障诊断. 为提高故障诊断的准确率, 降低随机性对分类算法的影响 …

Multi-objective optimization problems (MOPs) contain two or three objectives which need to
be optimized simultaneously. Rather than having a single optimal solution, MOPs have a set …