kNN 一直过度使用一个标签

Question

我正在使用 kNN 对标记图像进行一些分类。分类完成后，我输出一个混淆矩阵。我注意到一个标签 bottle 被错误应用的频率更高。

我取下标签并再次测试，但后来发现另一个标签 shoe 应用不正确，但上次没问题。

应该没有规范化，所以我不确定是什么导致了这种行为。测试表明，无论我删除多少标签，它都会继续。不完全确定 post 有多少代码，所以我会放一些应该相关的东西，然后粘贴剩下的东西。

def confusionMatrix(classifier, train_DS_X, train_DS_y, test_DS_X, test_DS_y):
    # Will output a confusion matrix graph for the predicion
    y_pred = classifier.fit(train_DS_X, train_DS_y).predict(test_DS_X)
    labels = set(set(train_DS_y) | set(test_DS_y))

    def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
        plt.imshow(cm, interpolation='nearest', cmap=cmap)
        plt.title(title)
        plt.colorbar()
        tick_marks = np.arange(len(labels))
        plt.xticks(tick_marks, labels, rotation=45)
        plt.yticks(tick_marks, labels)
        plt.tight_layout()
        plt.ylabel('True label')
        plt.xlabel('Predicted label')

    # Compute confusion matrix
    cm = confusion_matrix(test_DS_y , y_pred)
    np.set_printoptions(precision=2)
    print('Confusion matrix, without normalization')
    #print(cm)
    plt.figure()
    plot_confusion_matrix(cm)

    # Normalize the confusion matrix by row (i.e by the number of samples
    # in each class)
    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    print('Normalized confusion matrix')
    #print(cm_normalized)
    plt.figure()
    plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')

    plt.show()

主要功能的相关代码：

# Select training and test data
    PCA = decomposition.PCA(n_components=.95)
    zscorer = ZScoreMapper(param_est=('targets', ['rest']), auto_train=False)
    DS = getVoxels (1, .5)
    train_DS = DS[0]
    test_DS = DS[1]

    # Apply PCA and ZScoring
    train_DS = processVoxels(train_DS, True, zscorer, PCA)
    test_DS = processVoxels(test_DS, False, zscorer, PCA)
    print 3*"\n"

    # Select the desired features
    # If selecting samples or PCA, that must be the only feature
    featuresOfInterest = ['pca']
    trainDSFeat = selectFeatures(train_DS, featuresOfInterest)
    testDSFeat = selectFeatures(test_DS, featuresOfInterest)
    train_DS_X = trainDSFeat[0]
    train_DS_y = trainDSFeat[1]
    test_DS_X = testDSFeat[0]
    test_DS_y = testDSFeat[1]


    # Optimization of neighbors
    # Naively searches for local max starting at numNeighbors
    lastScore = 0
    lastNeightbors = 1
    score = .0000001
    numNeighbors = 5
    while score > lastScore:
        lastScore = score
        lastNeighbors = numNeighbors
        numNeighbors += 1
        #Classification
        neigh = neighbors.KNeighborsClassifier(n_neighbors=numNeighbors, weights='distance')
        neigh.fit(train_DS_X, train_DS_y)

        #Testing
        score = neigh.score(test_DS_X,test_DS_y )

    # Confusion Matrix Output
    neigh = neighbors.KNeighborsClassifier(n_neighbors=lastNeighbors, weights='distance')
    confusionMatrix(neigh, train_DS_X, train_DS_y, test_DS_X, test_DS_y)

Pastebin：http://pastebin.com/U7yTs3vs

Answer 1

问题的部分原因是我的轴被错误标记，当我以为我正在删除错误标签时，实际上我只是删除了一个随机标签，这意味着错误数据仍在分析中。修复轴并删除实际上是 rest 的错误标签：

我改的代码是： cm = confusion_matrix(test_DS_y , y_pred, labels)

基本上我根据我的有序标签列表手动设置顺序。

kNN 一直过度使用一个标签

kNN Consistently Overusing One Label

classification

machine-learning

knn

confusion-matrix