Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem. However, existing oversampling methods generally introduce noise examples into original datasets, especially when the datasets contain class overlapping regions. In this study, a new oversampling method named Constrained Oversampling is proposed to reduce noise generation in oversampling. This algorithm first extracts overlapping regions in the dataset. Then Ant Colony Optimization is applied to define the boundaries of minority regions. Third, oversampling under constraints is employed to synthesize new samples to get a balanced dataset. Our proposal distinguishes itself from other techniques by incorporating constraints in the oversampling process to inhibit noise generation. Experiments show that it outperforms various benchmark oversampling approaches. The explanation for the effectiveness of our method is given by studying the impact of class overlapping on imbalanced learning.

No abstract

No abstract

No abstract

An accurate recognition of a dimensional variation pattern is very important for producing highquality body-in-white (BIW). The wide application of optical coordination measurement machines (OCMM) in vehicle factory provided massive online dimensional data for the variation pattern recognition. However, the massive serially correlated or autocorrelated and 100% measurement data generated from the OCMM challenge the traditional statistical process control (SPC) technology and the common variation recognition approaches. This paper presents a novel deep-learning method, long short-term memory neural network (LSTM NN), to recognize the variation pattern of the BIW OCMM online measurement data. A comparative study between the backpropagation neural network (BP NN) and the LSTM NN was implemented, and the practicability of the proposed intelligent method was demonstrated by a case study. With the efficient use of time series information, the LSTM NN has a good performance in variation patterns' recognition and high practicability in improving the quality of the BIW.INDEX TERMS Variation pattern recognition, long short-term memory neural network (LSTM NN), bodyin-white (BIW), online measurement data, deep learning.