请修改以上代码以提高准确率

以下是一些可能的修改方式，可以尝试以提高代码的准确率：

1. 特征选择：在加载和处理特征时，可以使用一些特征选择方法，例如，使用SelectKBest或SelectFromModel函数，选择最具有代表性和区分度的特征。具体实现可以参考sklearn库中的特征选择模块。

from sklearn.feature_selection import SelectKBest, f_classif

def DE_svm(folder_path, num_features):
    # 样本加载处理
    de_LDS_feature_dict, de_LDS_label_dict = build_extracted_features_dataset(folder_path, 'de_LDS', 'gamma')
    accuracy = 0
    for key in de_LDS_feature_dict.keys():
        print('当前处理到 experiment_{}'.format(key))
        cur_feature = de_LDS_feature_dict[key]
        cur_label = de_LDS_label_dict[key]
        train_feature = []
        train_label = []
        test_feature = []
        test_label = []
        for trial in cur_feature.keys():
            if int(trial) < 10:
                train_feature.extend(cur_feature[trial])
                train_label.extend(cur_label[trial])
            else:
                test_feature.extend(cur_feature[trial])
                test_label.extend(cur_label[trial])
        # 特征选择
        selector = SelectKBest(f_classif, k=num_features)
        selector.fit(train_feature, train_label)
        train_feature = selector.transform(train_feature)
        test_feature = selector.transform(test_feature)
        # 定义 svm 分类器
        svc_classifier = svm.SVC(C=0.8, kernel='rbf')
        svc_classifier.fit(train_feature, train_label)
        predict_label = svc_classifier.predict(test_feature)
        print(confusion_matrix(test_label, predict_label))
        print(classification_report(test_label, predict_label))
        cur_accuracy = svc_classifier.score(test_feature, test_label)
        accuracy += cur_accuracy
        print('当前 experiment 的 accuracy 为：{}%'.format(100*cur_accuracy))
    print('所有 experiment 上的平均 accuracy 为：{}%'.format(100*accuracy / len(de_LDS_feature_dict.keys())))

2. 超参数优化：可以使用GridSearchCV或RandomizedSearchCV函数，对SVM模型的超参数进行优化。例如，可以设置不同的C值和kernel函数，选择最优的参数组合。以下是使用GridSearchCV进行超参数优化的示例代码：

from sklearn.model_selection import GridSearchCV

def DE_svm(folder_path, num_features):
    # 样本加载处理
    de_LDS_feature_dict, de_LDS_label_dict = build_extracted_features_dataset(folder_path, 'de_LDS', 'gamma')
    accuracy = 0
    for key in de_LDS_feature_dict.keys():
        print('当前处理到 experiment_{}'.format(key))
        cur_feature = de_LDS_feature_dict[key]
        cur_label = de_LDS_label_dict[key]
        train_feature = []
        train_label = []
        test_feature = []
        test_label = []
        for trial in cur_feature.keys():
            if int(trial) < 10:
                train_feature.extend(cur_feature[trial])
                train_label.extend(cur_label[trial])
            else:
                test_feature.extend(cur_feature[trial])
                test_label.extend(cur_label[trial])
        # 特征选择
        selector = SelectKBest(f_classif, k=num_features)
        selector.fit(train_feature, train_label)
        train_feature = selector.transform(train_feature)
        test_feature = selector.transform(test_feature)
        # 超参数优化
        param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf', 'poly']}
        svc_classifier = svm.SVC()
        clf = GridSearchCV(svc_classifier, param_grid)
        clf.fit(train_feature, train_label)
        print("Best parameters set found on development set:")
        print(clf.best_params_)
        predict_label = clf.predict(test_feature)
        print(confusion_matrix(test_label, predict_label))
        print(classification_report(test_label, predict_label))
        cur_accuracy = clf.score(test_feature, test_label)
        accuracy += cur_accuracy
        print('当前 experiment 的 accuracy 为：{}%'.format(100*cur_accuracy))
    print('所有 experiment 上的平均 accuracy 为：{}%'.format(100*accuracy / len(de_LDS_feature_dict.keys())))

3. 数据增强：可以使用imgaug库对图像进行增强，例如，随机旋转、翻转、缩放等。以下是对代码进行数据增强的示例代码：

import imgaug.augmenters as iaa

def DE_svm(folder_path, num_features):
    # 样本加载处理
    de_LDS_feature_dict, de_LDS_label_dict = build_extracted_features_dataset(folder_path, 'de_LDS', 'gamma')
    accuracy = 0
    for key in de_LDS_feature_dict.keys():
        print('当前处理到 experiment_{}'.format(key))
        cur_feature = de_LDS_feature_dict[key]
        cur_label = de_LDS_label_dict[key]
        train_feature = []
        train_label = []
        test_feature = []
        test_label = []
        for trial in cur_feature.keys():
            if int(trial) < 10:
                train_feature.extend(cur_feature[trial])
                train_label.extend(cur_label[trial])
            else:
                test_feature.extend(cur_feature[trial])
                test_label.extend(cur_label[trial])
        # 数据增强
        seq = iaa.Sequential([
            iaa.Affine(rotate=(-25, 25)),
            iaa.Flipud(0.5),
            iaa.Fliplr(0.5),
            iaa.Scale((0.5, 1.5))
        ])
        train_feature_aug = seq.augment_images(train_feature)
        train_label_aug = train_label * len(seq.augmentables)
        # 特征选择
        selector = SelectKBest(f_classif, k=num_features)
        selector.fit(train_feature_aug, train_label_aug)
        train_feature = selector.transform(train_feature_aug)
        test_feature = selector.transform(test_feature)
        # 超参数优化
        param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf', 'poly']}
        svc_classifier = svm.SVC()
        clf = GridSearchCV(svc_classifier, param_grid)
        clf.fit(train_feature, train_label_aug)
        print("Best parameters set found on development set:")
        print(clf.best_params_)
        predict_label = clf.predict(test_feature)
        print(confusion_matrix(test_label, predict_label))
        print(classification_report(test_label, predict_label))
        cur_accuracy = clf.score(test_feature, test_label)
        accuracy += cur_accuracy
        print('当前 experiment 的 accuracy 为：{}%'.format(100*cur_accuracy))
    print('所有 experiment 上的平均 accuracy 为：{}%'.format(100*accuracy / len(de_LDS_feature_dict.keys())))

需要注意的是，以上代码仅供参考，具体的修改方式需要根据实际情况进行测试和分析。