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資源簡介

臺灣大學林智仁 (Lin Chih-Jen) 教授等開發(fā)設計的 libsvm 工具箱提供了SVDD算法的MATLAB接口，其中兩個關鍵參數 c 和 g 直接影響SVDD的單值分類結果。 該代碼通過引入鯨魚優(yōu)化算法（Whale Optimization Algorithm，WOA），實現對 libsvm 工具箱中的SVDD算法的參數優(yōu)化，給出兩個實例代碼： 1. libsvm 工具箱提供的heart_scale data 2. 工業(yè)過程數據 WOA的具體描述可以參考以下文獻： （1）Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in engineering software, 2016, 95: 51-67. 該算法的提出者已經把代碼開源在mathworks。 注：（1）該代碼把 libsvm工具箱的svmtrain和svmpredict函數的名字分別改為libsvmtrain和libsvmpredict。 （2）WOA算法和其他群智能優(yōu)化算法一樣，容易陷入局部最優(yōu)，若尋優(yōu)結果出現異常，可以嘗試多運行幾次。

資源截圖

代碼片段和文件信息 

%%%%%%%%%%??????SVDD_WOA?（libsvm-3.23）???????????????%%%%%%%%%%%%%%%
%?Demo:?heart_scale?data
%?Created?on?1st?October?2019?by?Kepeng?Qiu.
%?---------------------------------------------------------------------%

clc?
clear?all
close?all
addpath（genpath（pwd））

global?traindata?trainlabel

%?heart_scale?data
[traindata?testdata?trainlabel?testlabel]?=?prepareData;


%?Parameter?setting?of?WOA
agent?=?10;????????????%?Number?of?search?agents
iteration?=?20;????????%?Maximum?numbef?of?iterations
lb?=?[10^-32^-4];?????%?Lower?bound?of?‘c‘?and?‘g‘
ub?=?[10^02^4];???????%?Upper?bound?of?‘c‘?and?‘g‘
dim?=?2;???????????????%?Number?of?Parameter
fobj?=?@woa_obj;???????%?objective?function

%?Parameter?optimization?using?WOA
[Best_score?Best_pos~]?=?WOA（agent?iteration?lb?ub?dim?fobj）;


%?Train?SVDD?hypersphere?using?the?optimal?parameters
cmd?=?[‘-s?5?-t?2?‘?‘-c?‘?num2str（Best_pos（11））?‘?-g?‘?...
????num2str（Best_pos（12））?‘?-q‘];
model?=?libsvmtrain（trainlabel?traindata?cmd）;

%?Test?
[predictlabel?accuracy?~]?=?libsvmpredict（testlabel?testdata?model）;




?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2019-10-16?13:05??data\
?????文件???????35936??2019-10-01?15:47??data\data_2.mat
?????文件???????28904??2012-03-19?16:52??data\heart_scale.mat
?????文件????????1128??2019-10-01?17:12??demo_1.m
?????文件????????1160??2019-10-01?17:12??demo_2.m
?????目錄???????????0??2019-10-16?13:05??func\
?????文件?????????646??2019-10-01?17:14??func\plotResult.m
?????文件?????????811??2019-10-01?16:30??func\prepareData.m
?????文件?????????684??2019-10-01?17:13??func\woa_obj.m
?????目錄???????????0??2019-10-16?13:05??libsvm-3.23\
?????文件????????9818??2018-07-15?22:16??libsvm-3.23\libsvmpredict.c
?????文件???????27648??2018-10-19?20:55??libsvm-3.23\libsvmpredict.mexw64
?????文件????????4060??2018-07-15?22:16??libsvm-3.23\libsvmread.c
?????文件???????13312??2018-10-19?20:55??libsvm-3.23\libsvmread.mexw64
?????文件???????12502??2016-12-26?19:56??libsvm-3.23\libsvmtrain.c
?????文件???????73728??2018-10-19?20:55??libsvm-3.23\libsvmtrain.mexw64
?????文件????????2326??2018-07-15?22:16??libsvm-3.23\libsvmwrite.c
?????文件???????12800??2018-10-19?20:55??libsvm-3.23\libsvmwrite.mexw64
?????文件?????????900??2018-10-19?20:55??libsvm-3.23\make.m
?????文件????????1240??2018-07-15?22:16??libsvm-3.23\Makefile
?????文件????????9815??2018-07-15?22:16??libsvm-3.23\README
?????文件????????8196??2018-07-15?22:16??libsvm-3.23\svm_model_matlab.c
?????文件?????????201??2018-07-15?22:16??libsvm-3.23\svm_model_matlab.h
?????目錄???????????0??2019-10-16?13:05??WOA\
?????文件????????3633??2016-02-10?23:43??WOA\func_plot.m
?????文件????????7725??2016-02-10?23:43??WOA\Get_Functions_details.m
?????文件????????1953??2016-02-10?23:43??WOA\initialization.m
?????文件????????3149??2016-03-03?15:32??WOA\main.m
?????文件????????4214??2016-02-10?23:43??WOA\WOA.m
?????文件?????1841081??2016-02-27?17:00??WOA\WOA.pdf
?????文件??????232696??2016-02-27?18:44??WOA\WOA.png
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