屬性約簡(jiǎn)準(zhǔn)則與約簡(jiǎn)信息損失的研究
發(fā)布時(shí)間：2019-07-27 08:23
【摘要】：屬性約簡(jiǎn)是粗糙集的重要研究?jī)?nèi)容,信息熵是度量信息量的方法.在研究絕對(duì)約簡(jiǎn)和幾種相對(duì)約簡(jiǎn)的基礎(chǔ)上,歸納出屬性約簡(jiǎn)的一般準(zhǔn)則.定義了基于條件屬性信息熵的屬性約簡(jiǎn)和基于聯(lián)合熵的屬性約簡(jiǎn),研究了幾種屬性約簡(jiǎn)與絕對(duì)約簡(jiǎn)之間的關(guān)系.定義了基于條件屬性信息熵的約簡(jiǎn)信息損失,澄清了屬性約簡(jiǎn)不損失信息的含糊觀念,指出了屬性約簡(jiǎn)只是在約簡(jiǎn)準(zhǔn)則意義下不損失信息,在信息熵意義下可能損失信息.為進(jìn)一步研究粗糙集、粒計(jì)算中屬性約簡(jiǎn)與分類夯實(shí)了信息論基礎(chǔ).
[Abstract]:Attribute reduction is an important research content of rough set, and information entropy is a method to measure information. Based on the study of absolute reduction and several relative reduction, the general criteria of attribute reduction are summarized. Attribute reduction based on conditional attribute information entropy and attribute reduction based on joint entropy are defined, and the relationship between several attribute reduction and absolute reduction is studied. This paper defines the reduction information loss based on conditional attribute information entropy, clarifies the vague concept that attribute reduction does not lose information, and points out that attribute reduction only does not lose information in the sense of reduction criterion, and may lose information in the sense of information entropy. In order to further study rough sets, attribute reduction and classification in grain computing consolidate the basis of information theory.
【作者單位】： 浙江師范大學(xué)數(shù)理與信息工程學(xué)院;浙江師范大學(xué)行知學(xué)院;同濟(jì)大學(xué)電子與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61572442,No.61203247,No.61273304,No.61573259,No.61472166) 浙江省自然科學(xué)基金(No.LY15F020012) 浙江省自然科學(xué)青年基金(No.Q13F020006)
【分類號(hào)】：TP18
本文編號(hào)：2519878
[Abstract]:Attribute reduction is an important research content of rough set, and information entropy is a method to measure information. Based on the study of absolute reduction and several relative reduction, the general criteria of attribute reduction are summarized. Attribute reduction based on conditional attribute information entropy and attribute reduction based on joint entropy are defined, and the relationship between several attribute reduction and absolute reduction is studied. This paper defines the reduction information loss based on conditional attribute information entropy, clarifies the vague concept that attribute reduction does not lose information, and points out that attribute reduction only does not lose information in the sense of reduction criterion, and may lose information in the sense of information entropy. In order to further study rough sets, attribute reduction and classification in grain computing consolidate the basis of information theory.
【作者單位】： 浙江師范大學(xué)數(shù)理與信息工程學(xué)院;浙江師范大學(xué)行知學(xué)院;同濟(jì)大學(xué)電子與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61572442,No.61203247,No.61273304,No.61573259,No.61472166) 浙江省自然科學(xué)基金(No.LY15F020012) 浙江省自然科學(xué)青年基金(No.Q13F020006)
【分類號(hào)】：TP18
【相似文獻(xiàn)】
相關(guān)期刊論文 前8條
1 柴振榮;計(jì)算機(jī)系統(tǒng)中如何減低信息損失的風(fēng)險(xiǎn)[J];管理科學(xué)文摘;1995年05期
2 張玉貴;;影像光學(xué)處理中的信息損失與補(bǔ)償[J];遙感信息;1987年02期
3 章輝;孫優(yōu)賢;;離散隨機(jī)模型降階的最小條件信息損失方法[J];控制理論與應(yīng)用;2005年06期
4 何紹華;方清華;;信息傳遞中的信息損失認(rèn)知與技術(shù)分析[J];情報(bào)理論與實(shí)踐;2006年06期
5 王智慧;許儉;汪衛(wèi);施伯樂(lè);;一種基于聚類的數(shù)據(jù)匿名方法[J];軟件學(xué)報(bào);2010年04期
6 楊曉春;劉向宇;王斌;于戈;;支持多約束的K-匿名化方法[J];軟件學(xué)報(bào);2006年05期
7 張付志;賈長(zhǎng)偉;徐玉辰;;基于聚類的l-多樣性匿名方法[J];燕山大學(xué)學(xué)報(bào);2012年01期
8 ;[J];;年期
相關(guān)碩士學(xué)位論文 前1條
1 許信輝;隱私數(shù)據(jù)發(fā)布下的集合型數(shù)據(jù)匿名化保護(hù)方法[D];上海交通大學(xué);2013年
，本文編號(hào)：2519878
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/2519878.html
最近更新
教材專著
