Dealing With Limited Datasets in Machine Learning
This article was published as a part of the Data Science Blogathon.
In machine learning and deep learning, the amount of data fed to the algorithm is one of the most critical factors affecting the model’s performance. However, in every machine learning or deep learning problem, it is impossible to have enough data to train the model accurately. In this type of scenario, dealing with the problem with a limited amount of data is important without losing accuracy.
This article will discuss some of the best strategies that are very useful for training machine learning and deep learning models with a limited amount of data, which relies on the data’s behavior and the type of data.
Let’s dive into it.
Dealing with Limited Unlabelled Data
The unlabelled data is a type of data in machine learning that does not have any target attributed defined, meaning that here we will have the training and testing datasets, but conditional variables will be absent.
To handle this type of data, we have many options to apply on it, some of which are discussed below:
1. User Defines the Labels:
In this strategy, the users or the field experts use their respective knowledge in the field to label the data by one-by-one observation.
This strategy could not be more efficient in dealing with unlabelled data, which will require a lot of time with human effort.
2. Use of Relative Datasets:
In this approach, the relative dataset or dataset with the same features as the limited data is searched to handle unlabeled datasets. Once a similar dataset is found, that particular dataset is used to label the limited amount of data.
3. Augmentation of User Labels:
In this approach, user-defined labels are used to label the dataset. Here the field experts define the brand for the dataset and label some of the parts of the limited observations, the different labeling of the dataset is done by augmenting the label that the field experts define. (Semi-Supervised Approach)
4. Embedding Approach:
In this approach, the labels and the data are converted into vectors, and then similar kinds of observations are classified based on their vector representations.
The embedding approach is the most efficient solution to handle unlabelled data, and hence it is widely used.
Dealing with Limited Labelled Data
The labeled data is mostly labeled and has target columns defined, meaning that this type of data has both independent and conditional columns.
Limited data is one of the biggest challenges to training machine learning and deep learning models with better accuracy. However, still, there are some methods using which we can handle this type of challenge properly.
- Traditional Machine Learning
- Shallow Neural Networks
- Medium Neural Networks
- Deep Neural Networks
The shallow and medium neural networks are the type of deep learning networks that are not designed deeply and do not have many hidden layers and neurons.
Experiments prove that the traditional and shallow deep neural networks are the type of algorithms whose performance tends to b constant after some amount of data is fed to them, which means that they can be used over limited information easily. On the other side, deep neural networks are data-hungry neural networks that perform better when we feed more data to the algorithm, but in the case of limited data problems, we can not use them efficiently.
1. Tree-Based Algorithms:
To handle limited labeled data, tree-based algorithms can be used to train an accurate machine-learning model. As tree-based algorithms are a type of non-parametric algorithm, the decision trees and other tree-based algorithms can be used here.
These algorithms sometimes outperform on limited datasets and return accurate results that even deep learning networks cannot provide
2. Ensemble Methods:
Ensemble methods are one of the best-performing machine learning methods of all time. In this method, multiple machine learning algorithms are used and ensembled to provide one final result.
Ensemble methods can be used here to handle the limited type of labeled data.
3. Shallow Neural Networks:
As we discussed above, deep neural networks are data-hungry neural networks that outperform when we feed more and more data to the algorithms. Conversely, shallow, deep neural networks are algorithms whose performance tends to be constant after some data is fed.
We can use Shallow neural networks to handle limited labeled data. There is a better performance than ever from external networks if tuned well and if the behavior of the data is in favor of the neural network training.
In this article, we discussed several strategies for dealing with limited datasets; We discussed different methods for dealing with limited labeled and unlabeled datasets. Knowing this strategy will help one to handle limited data efficiently and will be able to achieve higher accuracies on limited data.
Some Key Takeaways from this article are:
1. Limited data is one of the most complex challenges in machine learning, and it should be handled properly to avoid errors in the model.
2. The traditional machine learning algorithms and shallow neural networks outperform on limited data and can be used if the limited data is properly labeled.
3. If the data is limited and not properly labeled, then field experts can play a crucial role in this problem. We can use the semi-supervised and embedding approach in this type of case.
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