Cancer classification with data augmentation based on generative adversarial networks

Accurate diagnosis is a significant step in cancer treatment.Machine learning can support doctors in prognosis decision-making,and its performance is always weakened by the high dimension and small quantity of genetic data.Fortunately,deep learning can effectively process the high dimensional data with growing.However,the problem of inadequate data remains unsolved and has lowered the performance of deep learning.To end it,we propose a generative adversarial model that uses non target cancer data to help target generator training.We use the reconstruction loss to further stabilize model training and improve the quality of generated samples.We also present a cancer classification model to optimize classification performance.Experimental results prove that mean absolute error of cancer gene made by our model is 19.3%lower than DC-GAN,and the classification accuracy rate of our produced data is higher than the data created by GAN.As for the classification model,the classification accuracy of our model reaches 92.6%,which is 7.6%higher than the model without any generated data.

Exploit latent Dirichlet allocation for collaborative filtering

Previous work on the one-class collaborative filtering （OCCF） problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression （CTR）, usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation （LDA） model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user＇s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation metrics.We further combine this method with a matrix factorizationbased method to tackle the multi-class collaborative filtering （MCCF） problem, which also achieves better performance on predicting user ratings.