FuzzyKNN matlab code

最新推荐文章于 2022-11-29 20:46:04 发布

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本文介绍了一种改进的K近邻算法——模糊K近邻(fuzzy KNN)的实现方法。该算法通过考虑距离权重及模糊参数调整来提高分类准确性。在训练样本集上进行搜索，计算测试样本与训练样本之间的欧氏距离，并基于距离计算每个测试样本的交互与非交互得分，最终确定预测标签。

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function predict_labels = fuzzyKNN(train_samples, k, fa, test_features)
% train_samples : samples_num * (feature_num + 1)
    feature_numPlus1 = size(train_samples, 2);
    train_features = train_samples(:, 1:feature_numPlus1-1);
    train_labels = train_samples(:, feature_numPlus1);
    [idx, dists] = knnsearch(train_features,test_features,'dist','euclidean','k', k);
    
    predict_labels = zeros(size(test_features, 1), 1);
    for tst = 1:size(test_features, 1)
        score_interaction = 0.0;
        score_noninteraction = 0.0;
        for R=1:10
            if train_labels(idx(tst, R), 1) == 1
                score_interaction = score_interaction + dists(tst, R).^(-2/(fa-1));
            else
                score_noninteraction = score_noninteraction + dists(tst, R).^(-2/(fa-1));
            end
        end
        if score_interaction > score_noninteraction
            predict_labels(tst, 1) = 1.0;
        else
            predict_labels(tst, 1) = 2.0;
        end
    end
end