Decision tree techniques have been widely used to build classification models as such models closely resemble human reasoning and are easy to understand. This paper describes basic decision tree issues and current research points. Of course, a single article cannot be a complete review of all algorithms (also known induction classification trees), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

Decision trees: a recent overview

Parameter optimization is the core of PID controller design, for which, an approach based on artificial fish swarm algorithm(AFSA) is proposed. Experimental results verify the validity and feasibility of the proposed approach, and the results show that the approach is better than PSO algorithm, it can obtain the parameters with better control performances faster response speed, shorter adjustment time and smaller overshoot.

PID Controller Parameters Optimization Based on Artificial Fish Swarm Algorithm

The content based image retrieval system may discovery user needed image fleetly and accurately from image database. In order to improve content based image retrieval system performance, this paper introduces a new method which realizes image retrieval by multi-feature fusion. Color feature is extracted based on color histogram, texture feature is extracted based on gray co-occurrence matrix, and shape feature is represented by moment invariants. In accomplished image retrieval system, three kinds of low-level visual features of image are fused correctly, and relevance feedback and weight regulation algorithm are utilized to increase the precision of image retrieval. The feature extraction, system structure, similarity calculation, and working flow are also discussed in detail. The experimental result indicates that multi-feature fusion can improve the accuracy rate and recall ratio of image retrieval, and increase system efficiency, validity, and flexibility.

Image retrieval system based on multi-feature fusion and relevance feedback

Compressed sensing (CS) is a new area of signal processing for simultaneous signal sampling and compression. The CS principle can reduce the computation complexity at the encoder side and transmission costs, but has huge computation load at the decoder. In this paper, a simple block-based compressed sensing reconstruction for still images is proposed. Firstly, original image is divided into small blocks, and each block is sampled independently. Secondly, the image block is divided into flat and non-flat block, and processed with different ways. Finally, mean filter and an improvement total-variation (TV) method is sued to optimize image. Simulation results show that the proposed algorithm can effectively remove the blocking artifacts and reduce the computation complexity.

Signal reconstruction based on block compressed sensing

Decision tree techniques have been widely used to build classification models. In this study, we attempted to increase the prediction accuracy of a decision tree model by integrating local application of Naive Bayes classifier. We performed a large-scale comparison with other state-of-the-art algorithms on 30 standard benchmark datasets and the proposed method gave statistical better accuracy in some cases.

A hybrid decision tree classifier

决策树是数据挖掘任务中分类的常用方法.在构造决策树的过程中,节点划分属性选择的度量直接影响决策树分类的效果.基于粗糙集的属性频率函数方法度量属性重要性,并用于分枝划分属性的选择和决策树的预剪枝,提出一种决策树学习算法.同时,为了能处理教值型属性,利用数据集的统计性质为启发式知识,提出了一种改进的数值型属性信息熵离散化算法.实验结果表明,新的离散化方法计算效率有明显提高,新的决策树算法与基于信息熵的决策树算法相比较,结构简单,且能有效提高分类效果.

Decision tree algorithm using attribute frequency splitting and information entropy discretization

A method of memory-optimized is presented for collision detection algorithm based on bounding-volume.Collision detection algorithm based on AABB tree is improved from a space perspective.From the constructing process of AABB tree,the amount of byte of AABB bounding-volume for internal node is reduced.We wipe the bounding-volume out from leaf nodes structure based on a fast triangle-triangle intersection test algorithm,and then wipe the leaf nodes out.We optimize the storage of AABB bounding-volume and leaf node at the same time.The direct result is that it can save a large amount of space and speed up the algorithm.

Memory-optimized of collision detection algorithm based on bounding-volume hierarchies


 经导管主动脉瓣置换术（TAVR）已成为老年主动脉瓣狭窄的主流治疗方式。股动脉入路是TAVR的首选入路，90%以上的患者可经股动脉完成TAVR，替代入路包括经锁骨下动脉、经腋动脉、经颈动脉、经升主动脉和经心尖入路。对于常规经股动脉或上述替代入路均不适合的患者，近年来国际上报道了经腔静脉入路，但该入路操作步骤相对复杂、技术要求高，在国内尚未见报道。该文报道1例具有明确TAVR指征，但股动脉入路和常规替代入路均不适合的主动脉瓣狭窄病例，通过建立腔静脉至腹主动脉通路成功完成TAVR，植入球扩式脉瓣系统。.


[A case of transcaval transcatheter aortic valve replacement].

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Decision tree algorithm using attribute frequency splitting and information entropy discretization

Memory-optimized of collision detection algorithm based on bounding-volume hierarchies

[A case of transcaval transcatheter aortic valve replacement].