Imbalance-induced energy loss accounts for a significant part of in low-voltage distribution networks. However, due to the prohibitive cost full monitoring, high-resolution time-series data feeder currents are usually absent accurate computation. This letter presents novel method estimate probability imbalance-induced loss. In proposed method, only line resistance and statistical (mean covarian...

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is a...

Most of the existing methods for unbalanced data classification only consider about the situation of imbalance between classes but don't consider about the situation within the class, thus affect the final classification results. In order to eliminate the imbalance within the class, put forward the cluster algorithms based on DBSACN algorithm to process the imbalance problem within the class. T...

Automatic Action Unit (AU) intensity estimation is a key problem in facial expression analysis. But limited research attention has been paid to the inherent class imbalance, which usually leads to suboptimal performance. To handle the imbalance, we propose (1) a novel multiclass under-sampling method and (2) its use in an ensemble. We compare our approach with state of the art sampling methods ...

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and t...

In classification or prediction tasks, data imbalance problem is frequently observed when most of samples belong to one majority class. Data imbalance problem has received a lot of attention in machine learning community because it is one of the causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based boosting algorithm (GMBoost) to resolv...