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prologue
In machine learning and deep learning, the amount of data fed to an algorithm is one of the most important factors affecting model performance. However, for all machine learning or deep learning problems, it is impossible to have enough data to train a model accurately. In this type of scenario, handling the problem with a limited amount of data becomes important without losing precision.
This article describes some of the best strategies that are very useful for training machine learning and deep learning models with limited amounts of data that depend on data behavior and data type.
Let’s dive into it.
Processing limited unlabeled data
Unlabeled data is a type of machine learning data that has no defined target attribute. So here we have a training dataset and a test dataset, but no conditional variables.
There are many options that can be applied to handle this type of data. Some of them are described below.
1. User defines labels.
In this strategy, users or field experts use their knowledge in the field to observe and label the data piece by piece.
This strategy is not as efficient for processing unlabeled data. This requires a lot of human effort.
2. Using relative datasets:
In this approach, datasets with the same features as relative or limited data are searched to process unlabeled datasets. Once we find a similar dataset, we use that particular dataset to label a limited amount of data.
3. Extended user label:
This approach labels the dataset using user-defined labels. Here, field experts define brands for the dataset and label some of the limited observations. Different labeling of datasets is done by extending labels defined by field experts. (semi-supervised approach)
4. Embedding approach:
In this approach, the labels and data are transformed into vectors, and similar kinds of observations are classified based on their vector representations.
The embedding approach is widely used as it is the most efficient solution for processing unlabeled data.
Handling Limited Labeled Data
Most of the labeled data is labeled and has target columns defined. That is, this type of data has both independent and conditional columns.
Limited data is one of the biggest challenges to more accurately train machine learning and deep learning models. However, there are still some methods that can handle this kind of challenge well.
- traditional machine learning
- shallow neural network
- moderate neural network
- deep neural network
Shallow and medium neural networks are types of deep learning networks that are not designed to be deep and do not have many hidden layers or neurons.
Experiments have shown that traditional shallow deep neural networks, a type of algorithm that tends to have constant performance when fed with a constant amount of data, are easy to use with limited information. increase. Deep neural networks, on the other hand, are data-hungry neural networks, and although feeding more data into the algorithm improves performance, it is important to use them efficiently for problems with limited data. You can not.
1. Tree-based algorithm:
To process limited labeled data, tree-based algorithms can be used to train accurate machine learning models. Decision trees and other tree-based algorithms can be used here, as tree-based algorithms are a type of nonparametric algorithm.
These algorithms perform well on limited datasets and sometimes return accurate results that even deep learning networks cannot provide.
2. Ensemble method:
The ensemble method is one of the most performant machine learning methods to date. In this method, multiple machine learning algorithms are used and combined into one final result.
Ensemble methods can be used here to handle limited types of labeled data.
3. Shallow Neural Networks:
As explained above, deep neural networks are data-hungry neural networks, and feeding more data into the algorithm improves performance. Conversely, shallow and deep neural networks are algorithms that tend to have constant performance once fed with data.
Shallow neural networks can be used to process limited labeled data. If properly tuned and the data behavior is suitable for neural network training, the performance from the external network will be better than ever.
Conclusion
In this article, we’ve discussed several strategies for working with limited datasets. We have discussed various methods for handling limited labeled and unlabeled datasets. Knowing this strategy can help you process limited data efficiently and achieve greater accuracy with limited data.
A few important point From this article:
1. Limited data is one of the most complex challenges in machine learning and must be properly handled to avoid model errors.
2. Traditional machine learning algorithms and shallow neural networks perform well on limited data and can be used when limited data is well labeled.
3. When data are limited and not properly labeled, field experts can play an important role in this issue. A semi-supervised embedding approach can be used for this type of case.
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