How to handle imbalanced datasets in Keras?

12 months ago

Emily Johnson

2 minutes

There are several methods for handling imbalanced datasets in Keras.

Weighting of classes in a classification model.
train the model
weight distribution within a class

class_weight = {0: 1, 1: 10}  # 设置类别权重，例如少数类别设置更大的权重
model.fit(X_train, y_train, class_weight=class_weight)

Over-sampling/under-sampling: Balancing a dataset can be achieved by either over-sampling (increasing samples of minority class) or under-sampling (reducing samples of majority class). This can be done using RandomOverSampler and RandomUnderSampler from the imbalanced-learn library to over-sample and under-sample, respectively, before using the processed data for model training.
By utilizing a custom loss function, you can define your own loss function based on the specific situation, allowing it to place more emphasis on samples from minority classes. Using the backend module in Keras, you can define a custom loss function and then specify it during model compilation.

import keras.backend as K

def custom_loss(y_true, y_pred):
    # 自定义损失函数，例如将损失函数在少数类别样本上加权
    loss = K.binary_crossentropy(y_true, y_pred)  # 二分类交叉熵损失
    return loss

model.compile(loss=custom_loss, optimizer='adam')

Using the above methods can effectively handle imbalanced datasets and improve the model’s performance on minority classes.