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⛄ 内容介绍
在不平衡数据集中,由于少类样本和多类样本的不平衡,在分类过程中容易产生难以分类和错误分类的现象.针对不平衡数据集的分类特点,设计出一种组合分类器,适用于不平衡数据集的分类
⛄ 完整代码
%% -------------------------------------------------------------------
% Descrption : AdaBoost Classification Demo
% Reference : http://staff.ustc.edu.cn/~xjchen99/teaching/ImageUnderstanding/Robust%20real-time%20face%20detection.ppt
%% -------------------------------------------------------------------
function adaboostDemo
close all
clear all
clc
%% Settings
weakLearnerNum = 16; % how many weak classifiers will learn during AdaBoost training
displayTrainingProcess = 1; % whether display training process
%% generate samples
r = sqrt(rand(100,1)); % radius
t = 2*pi*rand(100,1); % angle
positive = [r.*cos(t), r.*sin(t)];
r = sqrt(3*rand(100,1)+1); % radius
t = 2*pi*rand(100,1); % angle
negative = [r.*cos(t), r.*sin(t)];
figure(1)
hold on
plot(positive(:,1),positive(:,2),'.r')
plot(negative(:,1),negative(:,2),'.b')
ezpolar(@(x)1);ezpolar(@(x)2);
axis equal
hold off
%% AdaBoost training process
data = [positive; negative];
labels = [ones(size(positive,1),1); -ones(size(negative,1),1)];
weights = [ones(size(positive,1),1) ./ (2*size(positive,1)); ones(size(negative,1),1) ./ (2*size(negative,1))];
weakLearners = zeros(weakLearnerNum,4); % [dimension, threshold, polarity, errorRate]
for t = 1 : weakLearnerNum
% re-normalize the weights
weights = weights ./ sum(weights);
% select best classifier
[dimension, threshold, polarity, errorRate] = selectBestClassifier(data, labels, weights);
% update sample weights
weights = updateSampleWeights(data, labels, weights, dimension, threshold, polarity, errorRate, displayTrainingProcess);
% load weak classifier
weakLearners(t,:) = [dimension, threshold, polarity, errorRate];
end
%% AdaBoosting predict process
figure(3)
hold on
for i = 1 : size(data,1)
% classify by weak classifiers
x = data(i,:);
if adaboostingPredict(x, weakLearners) == 1
plot(x(1), x(2), '.r');
else
plot(x(1), x(2), '.b');
end
end
ezpolar(@(x)1);ezpolar(@(x)2);
axis equal
hold off
%% Classify sample
function label = adaboostingPredict(x, weakLearners)
dimensions = weakLearners(:,1);
thresholds = weakLearners(:,2);
polarities = weakLearners(:,3);
errorRates = weakLearners(:,4);
beta = errorRates ./ (1-errorRates);
alpha = log(1./beta);
features = x(dimensions);
hypothesis = (polarities' .* features)' < (polarities .* thresholds);
label = (alpha' * hypothesis) > (sum(alpha) * 0.5);
%% Update samples weights
function weights = updateSampleWeights(data, labels, weights, dimension, threshold, polarity, errorRate, displayTrainingProcess)
% classify data by current threshold
positive = find(polarity.*data(:,dimension) < polarity*threshold);
negative = find(polarity.*data(:,dimension) >= polarity*threshold);
% find the correct samples
positive(find(labels(positive) ~= 1)) = [];
negative(find(labels(negative) ~= -1)) = [];
corrects = [positive; negative];
weights(corrects) = weights(corrects) .* (errorRate / (1-errorRate));
% plot current weak classifier and weighted samples
if displayTrainingProcess == 1
figure(2)
clf(figure(2),'reset')
hold on
for i = 1 : size(data,1)
color = 'y'; % default : incorrect classfied samples
if ~isempty(find(positive == i)) % correct classified positive samples
color = 'r';
end
if ~isempty(find(negative == i)) % correct classified negative samples
color = 'b';
end
plot(data(i,1), data(i,2), 'o', 'MarkerEdgeColor', 'k', 'MarkerFaceColor', color, 'MarkerSize', weights(i)*size(data,1)*10);
end
if dimension == 1
line([threshold, threshold], [-2, 2], 'LineWidth', 4, 'Color', [.8 .8 .8])
else
line([-2, 2], [threshold, threshold], 'LineWidth', 4, 'Color', [.8 .8 .8])
end
ezpolar(@(x)1);ezpolar(@(x)2);
axis equal
hold off
pause
end
%% Select best classifier
function [bestDim, bestThreshold, bestPolarity, bestErrorRate] = selectBestClassifier(data, labels, weights)
bestDim = 0;
bestThreshold = 0;
bestPolarity = 0;
bestErrorRate = sum(weights);
for dim = 1 : size(data,2)
[threshold, polarity, errorRate] = buildFeatureClassifier(data(:,dim), labels, weights);
if errorRate <= bestErrorRate
bestDim = dim;
bestThreshold = threshold;
bestPolarity = polarity;
bestErrorRate = errorRate;
end
end
%% Build feature classifier
function [bestThreshold, bestPolarity, bestErrorRate] = buildFeatureClassifier(data, labels, weights)
bestThreshold = 0;
bestPolarity = 0;
bestErrorRate = sum(weights);
% sort data
[data, index] = sort(data);
labels = labels(index);
weights = weights(index);
% generate possible splitters
splitters = data(1:end-1) + 0.5 * (data(2:end) - data(1:end-1));
for i = 1 : size(splitters,1)
threshold = splitters(i);
for polarity = -1 : 2 : 1 % polarity is (+) / (-)
positive = find(polarity.*data < polarity*threshold);
negative = find(polarity.*data >= polarity*threshold);
positive(find(labels(positive) == 1)) = []; % dimiss correct samples
negative(find(labels(negative) == -1)) = [];
incorrects = [positive; negative];
errorRate = sum(weights(incorrects));
if errorRate <= bestErrorRate
bestThreshold = threshold;
bestPolarity = polarity;
bestErrorRate = errorRate;
end
end
end
⛄ 运行结果
⛄ 参考文献
[1]董庆伟. "基于Adaboost算法的不平衡数据集分类效果研究." 长春师范大学学报 41.6(2022):4.
