Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

Many real-world applications have problems when learning from imbalanced data sets, such
as medical diagnosis, fraud detection, and text classification. Very few minority class …

Ensemble methods overcome the limitations of single machine learning techniques by
combining different techniques, and are employed in the quest to achieve a high level of …

In class imbalance learning problems, how to better recognize examples from the minority
class is the key focus, since it is usually more important and expensive than the majority …

Many real-world problems are usually unbalanced, where datasets present skewed class
distributions, such as failure diagnosis, spam detection, anomaly detection, fraud detection …

Misclassification is a critical problem in many machine learning applications. Since even the
classifier models with high accuracy (eg,> 95%) still introduce some misclassification error, it …

This thesis studies the diversity issue of classification ensembles for class imbalance
learning problems. Class imbalance learning refers to learning from imbalanced data sets …

由于推荐系统中用户对项目的评价数据具有多样性和稀疏性的特点, 传统的相似性度量算法不能
有效查找相似邻居, 本文提出一种基于优化欧氏距离的邻居相似度计算方法 …

To gain more valuable information from the increasing large amount of data, data mining
has been a hot topic that attracts growing attention in this two decades. One of the …

We here compare the performance (predictive accuracy and processing time) of different
neural network ensembles with that of nearest neighbor classifier ensembles. Concerning …