Parallel load balancing is studied for problems with certain bisection properties. A class of problems has bisectors if every problem p of weight w(p) in the class can be subdivided into two subproblems whose weight (load) is at least an -fraction of the original problem. A problem p is to be split into N subproblems such that the maximum weight among them is as close to w(p)=N as possible. It ...

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...

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

When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determining the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers feature-space distance and the class-imbalance ratio when it performs conformal transformation on a kernel function. Experimental...

Abstract Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class imbalance. For instance in online environments uplift is used optimally target ads discounts, but very few users ever end up clicking an ad or buying. One common approach deal with imbalance classification by undersampling dataset. In this w...

Intrusion detection technology for network attacks is developing rapidly with the development of artificial intelligence technology. Recently, machine learning-based methods that can detect new types have been developed. To improve classification performance rare classes in intrusion dataset, we study efficient data preprocessing method based on learning. The UNSW-NB15, a well-known used experi...