我目前正在使用免费的 UCI 乳腺癌 .arff
文件练习 WEKA 建模,根据这里的各种帖子,我能够将其准确率调整到 63% 到 73%。我在 Windows 7 Starter 机器上使用 WEKA 3.7.10
。
我使用属性选择来减少变量的数量,使用
InfoGainAttributeEval
和Ranker
。我选择了前五名,结果如下:Evaluator: weka.attributeSelection.InfoGainAttributeEval Search: weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N -1 Relation: breast-cancer Instances: 286 Attributes: 10 age menopause tumor-size inv-nodes node-caps deg-malig breast breast-quad irradiat Class Evaluation mode: 10-fold cross-validation === Attribute selection 10 fold cross-validation (stratified), seed: 1 === average merit average rank attribute 0.078 +- 0.011 1.3 +- 0.64 6 deg-malig 0.071 +- 0.01 1.9 +- 0.3 4 inv-nodes 0.061 +- 0.008 3 +- 0.77 3 tumor-size 0.051 +- 0.007 3.8 +- 0.4 5 node-caps 0.026 +- 0.006 5 +- 0 9 irradiat 0.012 +- 0.003 6.4 +- 0.49 1 age 0.01 +- 0.003 6.6 +- 0.49 8 breast-quad 0.003 +- 0.001 8.5 +- 0.5 7 breast 0.003 +- 0.002 8.5 +- 0.5 2 menopause
删除低排名变量后,我开始创建我的模型。我选择了多层感知器,因为它是我研究所依据的期刊所要求的算法。
suggestion of Bernhard Pfahringe 使用 0.1
作为 learning rate
和 momentum
以及指数因子 1, 2, 4, 8,对于隐藏节点
和epoch
等。
尝试使用该方法几次后,我注意到隐藏层使用 2 和二进制数的十进制等价形式,即。 512, 1024, 2048, ... 导致准确性提高。例如,隐藏节点
为 2,epoch
为 1024 等等。
我有一系列不同的结果,但到目前为止我得到的最高结果如下(使用 hidden node
2 和 epoch
16384:
Scheme: weka.classifiers.functions.MultilayerPerceptron -L 0.1 -M 0.1 -N 16384 -V 0 -S 0 -E 20 -H 2
Relation: breast-cancer-weka.filters.unsupervised.attribute.Remove-R1-2,7-8
Instances: 286
Attributes: 6
tumor-size
inv-nodes
node-caps
deg-malig
irradiat
Class
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Sigmoid Node 0
Inputs Weights
Threshold -2.4467109489840375
Node 2 2.960926490700117
Node 3 1.5276384018358489
Sigmoid Node 1
Inputs Weights
Threshold 2.446710948984037
Node 2 -2.9609264907001167
Node 3 -1.5276384018358493
Sigmoid Node 2
Inputs Weights
Threshold 0.8594931368555995
Attrib tumor-size=0-4 -0.6809394102558067
Attrib tumor-size=5-9 -0.7999278705976403
Attrib tumor-size=10-14 -0.5139914771540879
Attrib tumor-size=15-19 2.3071396030112834
Attrib tumor-size=20-24 -6.316868254289899
Attrib tumor-size=25-29 5.535754474315768
Attrib tumor-size=30-34 -12.31495416708197
Attrib tumor-size=35-39 2.165860489861981
Attrib tumor-size=40-44 10.740913335424047
Attrib tumor-size=45-49 9.102261927484186
Attrib tumor-size=50-54 -17.072392893550735
Attrib tumor-size=55-59 0.043056333044031
Attrib inv-nodes=0-2 9.578867366884618
Attrib inv-nodes=3-5 1.3248317047328586
Attrib inv-nodes=6-8 -5.081199984305494
Attrib inv-nodes=9-11 -8.604844224457239
Attrib inv-nodes=12-14 2.2330604430275907
Attrib inv-nodes=15-17 -2.8692154868988355
Attrib inv-nodes=18-20 0.04225234708199947
Attrib inv-nodes=21-23 0.017664071511846485
Attrib inv-nodes=24-26 -0.9992481277256989
Attrib inv-nodes=27-29 -0.02737484354173595
Attrib inv-nodes=30-32 -0.04607516719307534
Attrib inv-nodes=33-35 -0.038969156415242706
Attrib inv-nodes=36-39 0.03338452826774849
Attrib node-caps 6.764954936579671
Attrib deg-malig=1 -5.037151186065571
Attrib deg-malig=2 12.469858109768378
Attrib deg-malig=3 -8.382625277311769
Attrib irradiat 8.302010702287868
Sigmoid Node 3
Inputs Weights
Threshold -0.7428771456532647
Attrib tumor-size=0-4 3.5709673152321555
Attrib tumor-size=5-9 3.563713261511895
Attrib tumor-size=10-14 7.86118954430952
Attrib tumor-size=15-19 2.8762105204084167
Attrib tumor-size=20-24 4.60168522637948
Attrib tumor-size=25-29 -5.849391383398816
Attrib tumor-size=30-34 -1.6805815971562046
Attrib tumor-size=35-39 -12.022394228003419
Attrib tumor-size=40-44 11.922229608392747
Attrib tumor-size=45-49 -1.9939414047194557
Attrib tumor-size=50-54 -5.9801974214306215
Attrib tumor-size=55-59 -0.04909236196295539
Attrib inv-nodes=0-2 5.569516359775502
Attrib inv-nodes=3-5 -7.871275549119543
Attrib inv-nodes=6-8 3.405277467966008
Attrib inv-nodes=9-11 -0.3253699778307026
Attrib inv-nodes=12-14 1.244234346055825
Attrib inv-nodes=15-17 1.179311225120216
Attrib inv-nodes=18-20 0.03495291263409073
Attrib inv-nodes=21-23 0.0043299366591334695
Attrib inv-nodes=24-26 0.6595250300030937
Attrib inv-nodes=27-29 -0.02503529326219822
Attrib inv-nodes=30-32 0.041787638417097844
Attrib inv-nodes=33-35 0.008416652090130837
Attrib inv-nodes=36-39 -0.014551878794926747
Attrib node-caps 4.7997880904143955
Attrib deg-malig=1 1.6752746955482163
Attrib deg-malig=2 6.130488722916935
Attrib deg-malig=3 -6.989852429736567
Attrib irradiat 8.716254786514295
Class no-recurrence-events
Input
Node 0
Class recurrence-events
Input
Node 1
Time taken to build model: 27.05 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 210 73.4266 %
Incorrectly Classified Instances 76 26.5734 %
Kappa statistic 0.2864
Mean absolute error 0.3312
Root mean squared error 0.4494
Relative absolute error 79.1456 %
Root relative squared error 98.3197 %
Coverage of cases (0.95 level) 98.951 %
Mean rel. region size (0.95 level) 97.7273 %
Total Number of Instances 286
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.891 0.635 0.768 0.891 0.825 0.300 0.633 0.748 no-recurrence-events
0.365 0.109 0.585 0.365 0.449 0.300 0.633 0.510 recurrence-events
Weighted Avg. 0.734 0.479 0.714 0.734 0.713 0.300 0.633 0.677
=== Confusion Matrix ===
a b <-- classified as
179 22 | a = no-recurrence-events
54 31 | b = recurrence-events
我的问题是如何才能将此数据的准确度至少提高到 90%? 我是否必须进行过滤,使用另一种 MLP 输入参数模式?
我计划在学习如何执行此操作后使用另一组数据(它有大约 50 个变量和 100,000 个实例)。
最佳答案
对于这样的问题显然没有好的答案,但我会给你一些或多或少的使用 MLP 的一般性提示:
- 首先,为什么要在处理如此小的数据集时删除特征?特征选择在高维问题和/或计算量大的模型中很重要。乳腺癌和 MLP 都不是这样。
- 迭代次数是 MLP 的最差停止标准,您应该在验证误差上升时停止训练,而不是在一些固定的迭代次数之后停止
- 我不知道你使用什么成本函数,但最重要的部分是正则化,因为 MLP 容易过度拟合。一些吉洪诺夫正则化是最低要求。
- 针对此类问题使用多个隐藏层是完全多余的。特别是,由于梯度消失现象,在 MLP 中训练多个隐藏层通常是不可能的。
- 为了摆脱学习算法参数化的束缚,我还建议放弃朴素的算法并至少使用resillent propagation,事实证明它在许多应用中都非常有效。
关于java - 提高 WEKA 多层感知器模型的准确性,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/20320406/