Prognostic Value of microRNA-133a Expression and Its Clinicopathologic Significance in Non-Small Cell Lung Cancer: A Comprehensive Study Based on Meta-Analysis and the TCGA Database.

Background: The role of microRNA-133a (miR-133a) in non-small cell lung cancers (NSCLCs) is controversial. Thus, we conducted a comprehensive study based on meta-analysis and The Cancer Genome Atlas (TCGA) database.

Methods: Publications were searched in both English and Chinese databases, and meta-analysis was performed using Stata 12.

Ensemble of Rotation Trees for Imbalanced Medical Datasets.

Medical datasets are often predominately composed of "normal" examples with only a small percentage of "abnormal" ones and how to correctly recognize the abnormal examples is very meaningful. However, conventional classification learning methods try to pursue high accuracy by assuming that the number of any class examples is similar to each other, which lead to the fact that the abnormal class examples are usually ignored and misclassified to normal ones. In this paper, we propose a simple but effective ensemble method called ensemble of rotation trees (ERT) to handle this problem in imbalanced medical datasets.

Forest Pruning Based on Branch Importance.

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain.

Image superresolution reconstruction via granular computing clustering.

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules.

Granular computing classification algorithms based on distance measures between granules from the view of set.

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity.