You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less…

The class imbalance problem is a common challenge in machine learning, especially in classification

tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the

majority class) has much more examples than the other class (the minority class). The minority class

is often the more interesting or important class, such as failure incidents, fraud cases, or rare

diseases. However, most machine learning algorithms are designed to optimize the overall accuracy,

which can be biased towards the majority class and ignore the minority class. This can result in poor

predictive performance, especially for the minority class.

There are different techniques to deal with the class imbalance problem, such as data-level methods,

algorithm-level methods, and evaluation-level methods1. Data-level methods involve resampling the

original dataset to create a more balanced class distribution. There are two main types of data-level

methods: oversampling and undersampling. Oversampling methods increase the number of

examples in the minority class, either by duplicating existing examples or by generating synthetic

examples. Undersampling methods reduce the number of examples in the majority class, either by

randomly removing examples or by using clustering or other criteria to select representative

examples. Both oversampling and undersampling methods can be combined with upweighting or

downweighting, which assign different weights to the examples according to their class frequency, to

further balance the dataset.

For the use case of investigating failures of a production line component based on sensor readings,

the best option is to downsample the data with upweighting to create a sample with 10% positive

examples. This option involves randomly removing some of the negative examples (the majority

class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the

positive examples to compensate for their low frequency. This option can create a more balanced

dataset that can improve the performance of the classification models, while preserving the diversity

and representativeness of the original data. This option can also reduce the computation time and

memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with

upweighting to create a sample with 10% positive examples is the best option for this use case.

Reference:

A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks