Randomized experiments have been used to assist decisionmaking in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.

No abstract

In this study, a series of new concepts and improved genetic operators of a genetic algorithm (GA) was proposed and applied to solve mobile robot (MR) path planning problems in dynamic environments. The proposed method has two superiorities: fast convergence towards the global optimum and the feasibility of all solutions in the population. Path planning aims to provide an optimal path from a starting location to a target location, preventing collision or so-called obstacle avoidance. Although GAs have been widely used in optimization problems and can obtain good results, conventional GAs have some weaknesses in an obstacle environment, such as infeasible paths. The main ideas in this paper are visible space, matrix coding and new mutation operators. In order to demonstrate the superiority of this method, three different obstacle environments have been used and an experiment is conducted. This algorithm is effective in both static and dynamic environments.

Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D 1 and D 2. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss l wr , triplet loss l t , and adversarial loss l a. We optimize the discriminator D 1 to distinguish between real and fake by constructing an adversarial loss l a between the generated complementing images, original un-occluded images, and smallscale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss l c into D 2. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods. INDEX TERMS Facial expression recognition, partial occlusion, image complementation, Wasserstein generative adversarial network.