TunePareto-package(TunePareto)
TunePareto-package()所属R语言包：TunePareto
Multi-objective parameter tuning for classifiers
多目标参数整定分类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generic methods for parameter tuning of classification algorithms using multiple scoring functions
通用分类算法的参数整定的方法，使用多个评分功能
Details
详细信息----------Details----------
The methods of this package allow to assess the performance of classifiers with respect to certain parameter values and multiple scoring functions, such as the cross-validation error or the sensitivity. It provides the tunePareto function which can be configured to run most common classification methods implemented in R. Several sampling strategies for parameters are supplied, including Latin Hypercube sampling, quasi-random sequences, and evolutionary algorithms.
此程序包的方法允许评估的分类器的性能，相对于一定的参数值和多个计分的功能，如交叉验证错误或灵敏度。它提供了tunePareto功能，可配置为运行R.几个参数取样策略实施的最常见的分类方法提供，包括拉丁超立方采样，伪随机序列，进化算法。
Classifiers are wrapped in generic TuneParetoClassifier objects which can be created using tuneParetoClassifier. For state-of-the-art classifiers, the package includes the corresponding wrapper objects (see tunePareto.knn, tunePareto.tree, tunePareto.randomForest, tunePareto.svm, tunePareto.NaiveBayes).
分类器被包裹在通用TuneParetoClassifier对象可以使用tuneParetoClassifier的。对于国家的艺术分类，软件包还包括相应的包装对象（见tunePareto.knn，tunePareto.tree，tunePareto.randomForest，tunePareto.svm，tunePareto.NaiveBayes）。
The method tests combinations of the supplied classifier parameters according to the supplied scoring functions and calculates the Pareto front of optimal parameter configurations. The Pareto fronts can be visualized using plotDominationGraph, plotParetoFronts2D and plotObjectivePairs.
所提供的分类器参数的方法测试的组合，根据所供给的评分函数和计算最佳的参数配置的Pareto前沿。帕累托方面可以可视化使用plotDominationGraph，plotParetoFronts2D和plotObjectivePairs的。
A number of predefined scoring functions are provided (see predefinedObjectiveFunctions), but the user is free to implement own scores (see createObjective).
许多预定义的打分函数（见predefinedObjectiveFunctions），但用户可以自由实现自己的分数（见createObjective）。
（作者）----------Author(s)----------
Christoph M眉ssel, Hans Kestler
Maintainer: Hans Kestler <hans.kestler@uni-ulm.de>
参考文献----------References----------
实例----------Examples----------
# optimize the 'cost' and 'kernel' parameters of an SVM according[优化的“成本”和“内核”根据参数的SVM]
# to CV error and CV Specificity on the 'iris' data set[CV错误和CV特异性的IRIS数据集]
# using several predefined values for the cost[使用成本的几个预定义的值]
r <- tunePareto(data = iris[, -ncol(iris)],
labels = iris[, ncol(iris)],
classifier=tunePareto.svm(),
cost=c(0.001,0.01,0.1,1,10),
kernel=c("linear", "polynomial",
"radial", "sigmoid"),
objectiveFunctions=list(cvError(10, 10),
cvSpecificity(10, 10, caseClass="setosa")))
# print Pareto-optimal solutions[打印帕累托最优的解决方案]
print(r)
# use a continuous interval for the 'cost' parameter [使用连续时间间隔参数的“成本”]
# and optimize it using evolutionary algorithms and[并用进化算法和优化]
# parallel execution with snowfall[并行执行，降雪]
library(snowfall)
sfInit(parallel=TRUE, cpus=2, type="SOCK")
r <- tunePareto(data = iris[, -ncol(iris)],
labels = iris[, ncol(iris)],
classifier = tunePareto.svm(),
cost = as.interval(0.001,10),
kernel = c("linear", "polynomial",
"radial", "sigmoid"),
sampleType="evolution",
numCombinations=20,
numIterations=20,
objectiveFunctions = list(cvError(10, 10),
cvSensitivity(10, 10, caseClass="setosa"),
cvSpecificity(10, 10, caseClass="setosa")),
useSnowfall=TRUE)
sfStop()
# print Pareto-optimal solutions[打印帕累托最优的解决方案]
print(r)
# plot the Pareto fronts[绘制帕累托前沿]
plotDominationGraph(r, legend.x="topright")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-10-1 12:59:40
