层次内P-集合及其性质

P-集合是由内P-集合X~F(internal packet setsX~F)与外P-集合X~F(outerpacket setsX~F)构成的集合对,或者(X~F,X~F)是P-集合.利用P-集合理论,给出内P-集合的扩展模型—层次内P-集合,把内P-集合的依赖关系扩展到层次内P-集合中,并研究层次内P-集合的性质.层次内P-集合是普通内P-集合的扩展,提供了多角度、多层次分析和研究问题的方法.     

基于层次内P-集合的粒计算模型

在层次内P-集合基础上,给出层次内P-集合的粒度,层次内P-集合的粒度计算,从粒的角度研究层次内P-集合之间的关系.把普通P-集合扩展为层次P-集合,并研究层次P-集合的粒计算特征.     

函数内P-集合的副集及其区间生成规律

基于函数P-集合(S~F,S~F)的动态性、规律性,提出函数内P-集合的副集,给出函数内P-集合副集的区间生成结构、区间生成规律,给出内P-规律ω~F的区间拆分规律及其拆分度量,解决了函数内P-集合S~F状态规律受游弋于S~F边缘的元素(函数内P-集合的副集中的函数)的干扰,而呈现出来的动态规律(区间拆分规律)以及动态变化程度(拆分度量)的刻画等问题.最后以实例分析函数内P-集合副集及其区间生成规律在风险投资中的应用.     

内P-显性信息规律显性-隐性特征与显性信息规律发现

函数P-集合(function packet sets)是把函数概念引入到P-集合内(packet sets),改进P-集合得到的,函数P-集合具有动态特性,规律(函数)特性。函数P-集合是由函数内P-集合SF(function internal packet set SF)与函数外P-集合SF(function outer packet set SF)构成的函数集合对;或者,(SF,SF)是函数P-集合.利用函数内P-集合与生物遗传学中的显性,隐性概念交叉,渗透,给出内P-显性信息规律的显性-隐性特征,给出内P-显性信息规律的显性-隐性定理,给出内P-显性信息规律发现准则;利用这些结果,给出内P-显性信息规律发现的应用.     

P-集合与双信息规律生成

P-集合(pacdet sets)是由内P-集合X~F(internal packet set X~F)与外P-集合X~F(outer packet set X~F)构成的集合对.P-集合是把动态特性引入到普通集合中,利用普通集合(cantor set)被提出的.在一定的条件下,P-集合能够回到普通集合的原点.利用P-集合,给出双信息规律生成概念,给出双信息规律依赖的属性特征,提出双信息规律生成定理,辨识定理,给出应用.P-集合是研究动态信息系统的一个新的数学理论与数学方法.     

P-集合的随机特征

在P-集合概念的基础上,利用元素迁移的随机性,蛤出P-集合的随机生成和P-集合的强随机生成,讨论了P-集合的随机特性.     

P-集合的格论性质

P-集合是把动态特性引入到有限普通集合中,改进普通集合得到的,P-集合是由内P-集合(internal packet sets)与外P-集合(outer packet sets)构成的集合对.利用P-集合理论,给出只集合的依赖关系和度量,证明P-集合在依赖关系下构成格,进而给出基于格论的P-.集合的相关性质.     

内扰动与属性合取扩展规律挖掘-发现

扰动的存在使得系统标准输出规律变成非标准输出规律的现象等价于在扰动存在的条件下,非标准输出规律被挖掘-发现;利用函数P-集合模型与它的动态特征给出这个现象的理论研究.主要结果:给出函数P-集合的结构与规律扰动,扰动恢复概念;给出扰动度量;利用这些概念,给出内扰动与属性合取扩展定理;属性合取扩展与内扰动规律挖掘定理,内扰动与规律挖掘辨识定理,内扰动与规律挖掘属性不变性定理.     

内-遗传信息与它的内P-推理发现特征

P-集合(Packet sets)是由内P-集合X~F(internal packet setsX~F)与外P-集合X~F(outer packet setsX~F)构成的集合对;或者,(X~F,X~F)是P-集合.给定有限普通集合X={x_1,x_2,…,x_q},α={α_1,α_2,…,α_k}是X的属性集合;若在α内补充属性,则X变成内P-集合X~F={x_1,x_2,…,x_p},X内元素x_1,x_2,…,x_p被内-遗传到X~F内,P≤q,P,q∈N~+.内-遗传是P-集合的重要应用特征之一.利用内P-集合,给出内-遗传信息概念,内-遗传信息的遗传特征;利用内P-推理,给出内-遗传信息的内P-推理辨识与未知内-遗传信息的内P-推理发现.     

外P-信息融合和它的属性合取特征

P-集合(packet sets)是由内P-集合X~F(internal packet set X~F)与外P-集合X~F(outer packet set X~F)构成的集合对;或者,(X~F,X~F)是P-集合.利用外P-集合,给出外P-信息融合生成,外P-信息融合补充生成与外P-信息融合度量概念;给出外P-信息融合生成定理,外P-信息融合依赖定理;给出外P-信息融合还原定理;给出外P-信息融合的属性合取定理与属性合取压缩定理;给出属性合取压缩外P-信息融合发现原理.     

流的Ω-极限集

本文讨论流的Ω－极限集的性质,推广了ＣｏｎｌｅｙＣ。在文［１］中的一些结果。     

Hierarchical multilabel classification based on path evaluation

Multi-label classification assigns more than one label for each instance; when the labels are ordered in a predefined structure, the task is called Hierarchical Multi-label Classification (HMC). In HMC there are global and local approaches. Global approaches treat the problem as a whole but tend to explode with large datasets. Local approaches divide the problem into local subproblems, but usually do not exploit the information of the hierarchy. This paper addresses the problem of HMC for both tree and Direct Acyclic Graph (DAG) structures whose labels do not necessarily reach a leaf node. A local classifier per parent node is trained incorporating the prediction of the parent(s) node(s) as an additional attribute to include the relations between classes. In the classification phase, the branches with low probability to occur are pruned, performing non-mandatory leaf node prediction. Our method evaluates each possible path from the root of the hierarchy, taking into account the prediction value and the level of the nodes; selecting the path (or paths in the case of DAGs) with the highest score. We tested our method with 20 datasets with tree and DAG structured hierarchies against a number of state-of-the-art methods. Our method proved to obtain superior results when dealing with deep and populated hierarchies.     

集合概念的扩张与完善

从集合概念的内涵出发,剖析了单值集——Cantor集合、Fuzzy集合、Rough集合、可拓集合概念的内涵;剖析了复值集——Grey集合、未确知集合、Vague集合、泛灰(UG)集合和广义泛灰(GUG)集合的内涵;从而得出广义泛灰集合是内涵最深的集合概念;它具有极强的描述能力,可以描述客观存在的一切现象,故又是外延最广的集合概念.     

模糊集贴近度表示的一般规律

分析了模糊集贴近度理论,得到模糊集贴近度表示的几种形式,为贴近度的实际应用提供了极大的方便.     

On the Separation and Order Law of Cancellation for Bounded Sets

In this paper we introduce the separation law for convex sets. Moreover, we prove that for a locally convex topological vector space the order cancellation law and separation law are equivalent.     

Function S-rough Sets and Their Law Characteristic

Function S-rough sets(Function Singular rough sets) are defined by R-function equivalence class which has dynamic characteristic, and a function is s law, function S-rough sets have law characteristic. Function S-rough sets has these forms: function one direction S-rough sets, function two direction S-rough sets and dual of function one direction S-rough sets. This paper presents the law characteristic of function one direction S-rough sets and puts forward the theorems of law-chain-attribute and law-belt. Function S-rough sets is s new research direction of the rough sets theory.     

函数S-粗集与规律双向分离

函数S-粗集(function singular rough sets)是用R-函数等价类定义的,函数是一个规律,函数S-粗集具有规律特征.函数S-粗集推广了Z.Pawlak粗集.利用函数S-粗集,给出规律生成,规律分离的讨论,提出规律分离定理.给出的结果在投资分险规律估计中得到了应用.     

Differences of Convex Compact Sets in the Space of Directed Sets. Part I: The Space of Directed Sets

A normed and partially ordered vector space of so-called directed sets is constructed, in which the convex cone of all nonempty convex compact sets in R ⁿ is embedded by a positively linear, order preserving and isometric embedding (with respect to a new metric stronger than the Hausdorff metric and equivalent to the Demyanov one). This space is a Banach and a Riesz space for all dimensions and a Banach lattice for n=1. The directed sets in R ⁿ are parametrized by normal directions and defined recursively with respect to the dimension n by the help of a support function and directed supporting faces of lower dimension prescribing the boundary. The operations (addition, subtraction, scalar multiplication) are defined by acting separately on the support function and recursively on the directed supporting faces. Generalized intervals introduced by Kaucher form the basis of this recursive approach. Visualizations of directed sets will be presented in the second part of the paper.