Multi-class imbalanced data classification problem is common in the real world, but traditional binary classification methods cannot be directly applied. Existing solutions include designing new multi-class classification algorithm and dividing multi-class classification problem into binary classification problem. The latter includes two widely used strategies, namely one versus all (OVA) and one versus one (OVO). In this paper, we propose a combination method based on all and one (A&O), which is a combination of OVA and OVO, for multi-class imbalanced data classification problem. The method is developed by combining A&O and data balancing technique named SMOTE. Comparative experiments on 13 UCI datasets show that the proposed method performs well.
A multi-scale encoder algorithm is proposed for image edge detection, which takes the autoencoder as basic backbone structure. Three auto-encoders, each is responsible for processing an image of one scale, are organized together to perform image-to-image prediction by combining all multi-scale convolutional features. Taking the advantage of the multi-scale strategy and self-attention mechanism, the algorithm detects image edges from coarse to fine gradually, and succeeds in detection the edges missed easily in other works. There are two types of skip connections designed in the model structure. One is connected between encoder and decoder within an auto-encoder, worked as the residual function, the other is between auto-encoders, providing multi-scale knowledge about the edges. The loss function is composed of cross-entropy function and Dice coefficient term which is used to handle imbalanced training data. Experimental results evaluated from BSDS500 and BIPED datasets show the algorithm achieves scores of 0.847, 0.858, 0.884 on ODS, OIS, AP indices and output high-quality edges.
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