One of the most important tasks in pattern, machine learning, and data mining is
classification problem. Introducing a general classifier is a challenge for pattern recognition …

Time series forecasting (TSF) has been widely used in many application areas such as
science, engineering, and finance. The phenomena generating time series are usually …

以QuickBird 高分辨率遥感影像为主要数据源, 采用多尺度影像分割方法提取地物对象的光谱,
纹理和形状特征; 在此基础上, 构建基于随机森林(RF) 方法的遥感影像分类模型 …

Classifier ensemble techniques have been an active area of machine learning research in
recent years. The aim of combining classifier ensembles is to improve the accuracy of the …

Very large-scale biometric systems are becoming mainstream in nationwide identity cards
and mobile secure payment methods. As with other Big Data systems, biometric systems …

We consider distributed privacy-preserving data mining in large decentralized data locations
which can build several neural networks to form an ensemble. The best neural network …

With the support of multi-scale segmentation of object-oriented classification method,
features and characteristics variables were established and extracted by taking the …

This paper presents an unsupervised change detection approach for synthetic aperture
radar (SAR) images based on a multiobjective clustering algorithm and selective ensemble …

A commonly used strategy for solving a multi-class classification problem is to decompose
the original problem into several binary subproblems. The recently proposed method …

An ensemble is a composite model, combining the predictions from several other models.
Ensembles are known to be more accurate than single models. Diversity has been identified …