1 Introduction The class imbalance problem is a challenge to machine learning and data mining, and it has attracted significant research recent years. A classifier affected by the class imbalance problem for a specific data set would see strong accuracy overall but very poor performance on the minority class. The imbalance data sets are pervasive in real-world applications. Examples of these ki...

We participated in one task of TRECVID 2008, that is, the high-level feature extraction (HLFE). This paper presents our approaches and results on the HLFE task. We mainly focus on exploring the data imbalance learning in this year, and propose two methods for this problem: (1) adaptive borderline-SMOTE and under-sampling SVM (ABUSVM), and (2) concept category. Our approach can be divided into t...

introduction: one of the basic problems of hospital management is lack of an effective fiscal system, which causes in loose cost control. meanwhile, accounting techniques and economic analyses may help the managers to decide better in planning programs and investing fields. then, it’s time to have a better estimate of health care services costs in our competitive situation for the hospitals, in...

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our...

In this paper, we present new algorithms to balance the computation of parallel hash joins over heterogeneous processors in the presence of data skew and external loads. Heterogeneity in our model consists of disparate computing elements, as well as general purpose computing ensembles that are subject to external loading (e.g., a LAN connected workstation cluster). Data skew manifests itself as...

The psychobiological basis of reactive aggression, a condition characterized by uncontrolled outbursts of socially violent behavior, is unclear. Nonetheless, several theoretical models have been proposed that may have complementary views about the psychobiological mechanisms involved. In this review, we attempt to unite these models and theorize further on the basis of recent data from psycholo...

Up-sampling and down-sampling are the two most used methods in balancing the data when dealing with two class imbalance problem. However, none of the existing approaches to class rebalance take into account class information (e.g. distribution, within and between class distances, imbalance factor). This study presents initial results of up-sampling methods based on various approaches to aggrega...

IQ imbalance that is prominent in Decision-directed architecture can cause significant degradation in the performance of wireless communication systems. In this paper, a discussion of a new algorithm that uses both training and data symbols in a decision-directed fashion to jointly estimate and compensate for the effects of the channel and high receiver I/Q imbalance is presented.

The common challenge which is faced by much of the data clustering techniques is data complexity, which leads to many issues such as overlapping, lack of representative data and class imbalance. This may deteriorates the clustering process. The situation gets worse when the class imbalance is very high. To cluster such imbalanced data sets, better understandings of the dataset and efficient clu...

Running BWA in multithreaded mode on a multi-socket server results in poor scaling behaviour. This is because the current parallelisation strategy does not take into account the load imbalance that is inherent to the properties of the data being aligned, e.g. varying read lengths and numbers of mutations. Additional load imbalance is also caused by the BWA code not anticipating certain hardware...