# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
###### Abstract  
In training-based Machine Learning applications, the training data are frequently labeled by non-experts and expose substantial label noise which greatly alters the training models. In this work, a novel method for reducing the effect of label noise is introduced. The rules are created from expert knowledge to identify the incorrect non-expert training data. Using the gradient descent algorithm, the violating data samples are weighted less to mitigate their effects during model training. The proposed method is applied to the image classification problem using Manga109 and CIFAR-10 dataset. The experiments show that when the noise level is up to 50% our proposed method significantly increases the accuracy of the model compared to conventional learning methods.  
prior knowledge; noise datasets; label noise; weighting training data +
Footnote †: journal:  
of other samples in a leave-one-out framework. Furthermore, probabilistic models with a novel form have been used and also improve the performance of the classifier [13; 14].  
The second approach is called \"filter framework\" and the algorithms consist of two separated parts. First, the label noise detection detects the label noise in the training dataset. Second, the label noise processing part uses a technique to mitigate the label noise effect. The most beneficial advantage of this framework is that it facilitates to create new algorithms that combine the strength of each part. Outlier detection based algorithms detect if the label noise sample is corresponding to an outlier sample, whose anomaly score exceeds a predefined threshold [15; 16]. Many algorithms use a model that trained from the original training set to classify the training set itself to detect the label noise [17; 18]. This method might suffer from the egg-chicken dilemma, as the model is trained using the training set with label noise and it may lead to bad performance, causing bad prediction of label noise as well as bad overall performance. To avoid this problem, the ensembles method is used to detect label noise more precisely [19; 20]. \(K\)-fold cross-validation is used to group \(K\) different pairs of training and validation sets, and each pair is used to train \(m\) different models using \(m\) different algorithms. After that, each instance in the corresponding validation set is classified by \(m\) models to detect the label noise. After label noise is detected, the simplest solution is to remove it from the training set, and retrain the model from the data set with label noise removed. However, this may lead to removing a lot of data resulting in bad training performance. A more complex algorithm is to weight the samples in the training set, which comes from the fact that the effect of each sample on the model can be different. Refs. [21; 22] down-weight the data samples that are likely label noise and train the model from the weighted data set.  
The third approach exploits expert knowledge and put it into the learning process. Ref. [23] converts knowledge into first-order logic (FOL) as constraints and then optimizes loss function subjected to those conditions. The deep-learning network in [23] is modified for integrating the FOL into the system. Although the performance of the modified deep-learning algorithm is good, it requires the change of the network structure which may cause difficulty in practical implementation.