Improving the generalization performance of classifiers from data pre-processing perspective has recently received considerable attention in the machine learning community. Although many methods have been proposed in the past decades, most of them lack theoretical foundations and cannot guarantee better generalization performance of classifiers on processed datasets. To overcome this flaw, in this paper, we propose a method, which is supported by Bayesian decision theory and percolation theory, to improve generalization performance by removing Bayesian confusing samples (abbr. BCS). Specifically, for a training set, we define the samples that misclassified by the Bayesian optimal classifier as BCS and prove that a classifier trained on the training set after removing BCS can obtain better generalization performance. To find out BCS, we indicate that BCS can be identified according to the size of global homogeneous cluster, a set of samples with the same labels, based on percolation theory. Based on these analysis, we propose a method to construct global homogeneous clusters and remove BCS from the training set. Extensive experiments show that the proposed method is effective for a number of classical and state-of-the-art classifiers.

To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method.One of the key problems of this recognition method is to recognise text under complex package patterns.In addition, some products use dot matrix characters to reduce printing costs, which makes text extraction more difficult.We propose a character detection algorithm using DBNet as the base network, combined with the Convolutional Block Attention Module (CBAM) to improve its feature extraction of characters in complex contexts.After the character area has been located by the detection algorithm, it is intercepted and fed into a fully convolutional character recognition network to achieve print character recognition.We use ResNet as the backbone network and CTC loss for training.In addition, the CBAM module was added to the backbone network to enhance its recognition of dot matrix characters.The algorithm was finally deployed on the jetson nano.The experimental results show that the character detection accuracy reaches 97.9%, an improvement of 1.9% compared to the original network.As for the character recognition algorithm, the inference speed is doubled when deployed to the nano platform compared to the CRNN network, with an accuracy of 97.8%.

For a classification task, we usually select an appropriate classifier via model selection. How to evaluate whether the chosen classifier is optimal? One can answer this question via Bayes error rate (BER). Unfortunately, estimating BER is a fundamental conundrum. Most existing BER estimators focus on giving the upper and lower bounds of the BER. However, evaluating whether the selected classifier is optimal based on these bounds is hard. In this article, we aim to learn the exact BER instead of bounds on BER. The core of our method is to transform the BER calculation problem into a noise recognition problem. Specifically, we define a type of noise called Bayes noise and prove that the proportion of Bayes noisy samples in a data set is statistically consistent with the BER of the data set. To recognize the Bayes noisy samples, we present a method consisting of two parts: selecting reliable samples based on percolation theory and then employing a label propagation algorithm to recognize the Bayes noisy samples based on the selected reliable samples. The superiority of the proposed method compared to the existing BER estimators is verified on extensive synthetic, benchmark, and image data sets.

In this paper, we consider the problem of lacking theoretical foundation and low execution efficiency of the instance selection methods based on the k-nearest neighbour rule when processing large-scale data. We point out that the core idea of these methods can be explained from the perspective of Bayesian decision theory, that is, to find which instances are reducible, irreducible, and deleterious. Then, based on the percolation theory, we establish the relationship between these three types of instances and local homogeneous cluster (i.e., a set of instances with the same labels). Finally, we propose a method based on an accelerated k-means algorithm to construct local homogeneous clusters and remove the superfluous instances. The performance of our method is studied on extensive synthetic and benchmark data sets. Our proposed method can handle large-scale data more effectively than the state-of-the-art instance selection methods. All code and data results are available at https://github.com/CQQXY161120/Instance-Selection.