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optimal feature subset selection
相关语句
  最优特征子集选择
     OFFSS (Optimal Fuzzy-Valued Feature Subset Selection) is an optimal feature subset selection based on fuzzy-valued extension matrix.
     最优模糊特征子集选取OFFSS(Optimal Fuzzy-Valued Feature Subset Selection)是一种用模糊扩张矩阵进行最优特征子集选择的方法。
短句来源
     As optimal feature subset selection(OFSS) is an NP-hard Problem,it is of realistic significance to find an approximative algorithm.
     指出最优特征子集选择问题(OFSS)是个NP-Hard问题,寻找一个近似算法具有现实意义。
短句来源
  最优特征子集抽取
     Optimal Feature Subset Selection for Multi-class Problem Based on the Fuzzy Extension Matrix
     基于模糊扩张矩阵多类问题的最优特征子集抽取
短句来源
  “optimal feature subset selection”译为未确定词的双语例句
     Optimal Feature Subset Selection of Decision Tables
     决策表最优特征子集的选择——基于粗集理论的启发式算法
短句来源
     Optimal Feature Subset Selection Using Genetic Algorithms
     最优特征子集的遗传算法求解
短句来源
     AN ALGORITHM FOR THE OPTIMAL FEATURE SUBSET SELECTION
     一种最优特征集的选择算法
短句来源
     Optimal Feature Subset Selection for Multi-class Issue
     多类问题中最优特征子集选取的研究
短句来源
     (2) We propose a new definition of relevant features integrated with strong relevance and incremental useful relevance. Under this new definition, we design a novel optimal Feature Subset Selection (FSS) algorithm named SRRW based on the genetic algorithm and the Wrapper approach.
     (2) 提出了“强相关”和“增量有用相关”相结合的特征相关性定义,并在此基础上设计了一种基于遗传算法和Wrapper模型的新的最优特征子集选取算法——SRRW (Strong Relevant Restricted Wrapper)。
短句来源
更多       
  相似匹配句对
     Feature:
     本文特色:
短句来源
     Research on Optimal Algorithms of Feature Selection
     特征选择的优化算法研究
短句来源
     AN ALGORITHM FOR THE OPTIMAL FEATURE SUBSET SELECTION
     一种最优特征集的选择算法
短句来源
     Optimal Feature Subset Selection of Decision Tables
     决策表最优特征子集的选择——基于粗集理论的启发式算法
短句来源
     The Apcication of K-L Transformation on the Optimal Feature Descriptions of Debris
     K-L变换在磨粒特征参数优化中的应用
短句来源
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  optimal feature subset selection
The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment.
      


The feature subset selection is an important problem in machine learning, but the optimal feature subset selection is proves to be a NP hard one. Based on rough sets, a new heuristic algorithm is presented to solve the difficulty. To decision tables where the number of features reduces greatly after reduction, the algorithm is illustrated to be effective. Especially, it can give almost all the optimal solutions.

特征子集选择问题是机器学习的重要问题 .而最优特征子集的选择是NP困难问题 ,因此需要启发式搜索指导求解 .基于粗集理论 ,本文提出了一种新的决策表最优特征子集选择的启发式算法 .和以往的方法相比 ,这种算法简单实用 ,在一定条件下能够以较高的效率得到最优特征子集

Feature extraction and selection are important for recognition rate in intelligent recognition using technologies of digital image processing and pattern recognition. This paper introduces a cell recognition system of urine sediment micrographs. Genetic neural network is used to realize optimal feature subset selection simplification of classifier design and increase of classification efficiency and thus the satisfying result is got.

在使用数字图像处理和模式识别技术进行智能识别过程中,特征提取和优选成为识别率问题的关键。本文给出了一个尿沉渣显微图像识别系统,重点阐述了遗传神经网络的原理和其在尿沉渣细胞分类的特征优选中的作用,实验表明使用遗传神经网络算法可以找到最优特征子集,简化分类器的设计,提高分类效率,最终得到满意的尿沉渣识别效果。

Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques. In this paper, a newly integrated feature extraction algorithm based on GA and wavelet/wavelet packet (WP) transform is proposed for high dimensional data reduction and classification. The proposed algorithm combines the advantages of GA's global optimization and wavelet's multiresolution and multi-scale analysis. Hyperspectral signals...

Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques. In this paper, a newly integrated feature extraction algorithm based on GA and wavelet/wavelet packet (WP) transform is proposed for high dimensional data reduction and classification. The proposed algorithm combines the advantages of GA's global optimization and wavelet's multiresolution and multi-scale analysis. Hyperspectral signals are firstly transformed to feature domain by using a discrete wavelet or wavelet packet decomposition strategy. Since the discrete wavelet transform (DWT) is a linear transform, the DWT coefficients at specific scales could be directly used as linear features. Followed by the decomposition phase is optimal feature subset selection, in which the optimal feature subset acquired the best divergence is obtained according to interclass/intraclass distance of the training samples. This procedure is implemented by a Genetic Algorithm, with each possible feature subset encoded as chromosome. Fitness scores in GA are calculated and evaluated based on Jeffries-Matusita distance of the selected training samples. Hyperspectral data are classified with maximum likelihood classifier ( MLC). Experimental results show that the use of DWT/WP and GA-based feature extraction technique improves the overall classification accuracy by 1. 1%-6. 5% , as compared to the use of conventional feature extraction techniques, such as principal component analysis ( PCA) , Discriminant Analysis Feature Extraction ( DAFE) and Decision Boundary Feature Extraction ( DBFE).

高维遥感数据的分类与识别与传统的多光谱遥感分类技术具有明显的区别。本文提出了一种基于遗传算法和小波/小波包分析相结合的特征提取方法用于高维遥感数据降维与分类。该方法综合了遗传算法的全局优化和小波/小波包分析的多尺度、多分辨率的特点。首先,通过离散的小波变换(DWT)或小波包变换(WP)将高光谱信号变换到特征域进行光谱分解。由于DWT变换是一种线性变换,不同尺度的DWT系数可作为线性光谱特征。然后,对这些线性光谱特征利用遗传算法结合训练样本计算类内/类间距离搜索最优分类子集,其具体染色体编码取可能的特征号,适应度函数基于样本平均Jeffries-Matusita距离计算。所用的分类器采用最大似然分类器。试验结果表明该方法与常规特征提取算法如主成分变换(PCA)、判别分析特征提取(DAFE)、决策边界特征提取(DBFE)相比,能提高分类精度约1.1%-6.5%。

 
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