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1. Overview

In this tutorial, we’ll look at the ensemble learning method in machine learning.

Then, we’ll go over the common types of ensemble learning. Finally, we’ll walk through different ensemble learning applications.

2. Definition of Ensemble Learning

We create machine learning models to generate the best possible predictions for a particular situation. However, a single model may not make the best predictions and may be subject to errors such as variance and bias.

So, we may combine multiple models into a single model. Consequently, This is what is known as ensemble learning to decrease these errors and enhance the predictions.

Then, we will explore ensemble learning techniques that can be used to improve the machine learning process.

3. Common Types of Ensemble Learning

In this part, we’ll present the advanced ensemble learning techniques.

3.1. Bagging

Bagging is a combination of the words bootstrapping and aggregation. It merges the two into a single model. Also, Bagging is a technique that combines multiple models to get a more generalized result.

However, there’s a problem when it comes to training the models. When all models are trained on the same data and then combined, the results are likely to be fairly similar.

Bootstrapping is a sampling strategy that can tackle such a problem. It can be used to create subsets of the original dataset’s observations. The sample is carried out using a replacement method.

When we randomly selected an example from a population and then returned it to the population, this is known as sampling with replacement. The size of the subsets is the same as the original dataset. Also, we use Bags as a term to describe these subsets.

Here are the steps to the bagging process:

  • We created Bags using the bootstrapping method. As previously stated, sampling with replacement is carried out
  • We now perform bootstrap aggregation after creating bags (bagging). This approach makes use of the bags to figure out how the data in the dataset is distributed
  • What follows is the creation of base models on each of the bootstrapped subsets. It’s worth noting that the models run independently and in parallel to each other
  • The final step entails merging the results from all of the models to determine the final projections

The image below will aid in the understanding of the bagging process:

fig2

3.2. Boosting

Boosting involves using a collection of algorithms to convert weak learners into strong learners. A poor learner labels instances just slightly better than random guessing. A basic decision tree is an example. In addition, Weighted data is used on these weak learners. For misclassified data, the weighting is unique.

Boosting is done consecutively with each subsequent model to minimize the error of the model preceding it.

Let’s walk through the boosting procedure:

FIGURE5666

Boosting algorithms aim to enhance prediction accuracy by training a series of weak models, each one correcting for the shortcomings of the one before it (i.e., adjusting the weight).

In general, it combines the outputs of weak learners to generate a strong learner, which increases the model’s prediction power.

Boosting places a greater emphasis on cases that have been misclassified or have more mistakes as a result of previous weak rules.

3.3. Stacking

Stacking is a strategy for training a meta-classifier that leverages the predictions of many classifiers as new features. Many of the classifiers are classified as level one classifiers. We may define a meta-classifier alternatively as a classifier that incorporates the predictions of other classifiers.

To make this process more intuitive, let’s use an image:

 

figure3

We can see three level-one classifiers in the image above (C1. C2, and C3). We trained the classifiers individually. The classifiers make predictions once they have been taught. So, the meta-classifier is then trained using the predictions that were made. It’s best to have level one predictions from a portion of the training set that wasn’t utilized for training the level one classifier when stacking classifiers.

The goal is to keep information from what we’re trying to predict (target) from getting into the training set. To do this, we divided the training set into two halves. The level-one classifiers should be trained with the first half of the training set. We utilize the classifiers on the remaining half of the training data to create predictions once they’ve been trained.

Finally, the predictions trained the meta-classifier. It’s worth mentioning that regression models can also benefit from stacking. Stacking, like classification models, combines the predictions of many regression models using a meta-regressor.

The diagram below describes the technique better :

fig5

4. Ensemble Learning Applications

The number of applications for big ensemble learning has increased in recent years as computer power has expanded, allowing large ensemble learning to be trained in a reasonable amount of time.

In fact, ensemble classifiers have a variety of uses, including:

  • Medicine: this field effectively employed ensemble classifiers in neuroscience, proteomics, and medical diagnoses, such as the identification of neurocognitive disorders using MRI datasets and the categorization of cervical cytology
  • Emotion recognition: while most industry heavyweights in this sector, such as Google, Microsoft, and IBM, indicate that the underlying technology of their speech recognition is based on deep learning, speech-based emotion identification may also perform well using ensemble learning
  • Face recognition: face recognition deals with the identification or verification of a person based on their digital images and has lately become one of the most popular study topics in pattern recognition. This discipline always used ensemble learning techniques to achieve noteworthy performances
  • Intrusion detection: an intrusion detection system monitors computer networks or computer systems to identify intruder codes like an anomaly detection process. Ensemble learning is very effective in reducing overall error in monitoring systems
  • Computer security, Remote sensing, Fraud detection

5. Conclusion

Any machine learning task seeks to identify a single model that best predicts our desired outcome. Rather than creating a single model and hoping it is the best/most accurate predictor possible, ensemble methods use a variety of models and average them to get a single final model.

In this article, we’ve discussed the fundamentals of ensemble learnings. Also, we discussed different techniques of ensemble learning that allow improving the final classification results.

Then, we presented its importance in various fields.

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