Evidence integration credal classification algorithm versus missing data distributions

TL;DR: Zhang et al. as mentioned in this paper proposed distribution adaptation based on evidence theory (DAET) to improve the classification accuracy by combining the complementary information derived from both the source and target domains.

TL;DR: In this paper , the authors proposed a novel method, PIM estimator (PIME), for inferring the differential effect of treatment, which aims to estimate the average treatment effect using the three-way outcome array of a two-level design.

TL;DR: Wang et al. as discussed by the authors proposed a deep hyperspherical clustering (DHC) method for skin lesion medical image segmentation by combining deep convolutional neural networks and the theory of belief functions (TBF).

TL;DR: In this article , a comprehensive review of the approaches to tackle the missing data problem is discussed with a comprehensive discussion on the effectiveness of three imputation methods namely, imputation based on Multiple Linear Regression (MLR), Predictive Mean Matching (PMM), and Classification And Regression Tree (CART) in the context of subspace clustering.

TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.

TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.

TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.