Ensemble learning can help you make better decisions and solve many real-life challenges by combining decisions from several models.
Machine learning (ML) continues expanding its wings in multiple sectors and industries, whether it’s finance, medicine, app development, or security.
Training ML models properly will help you achieve greater success in your business or job role, and there are various methods to achieve that.
In this article, I’ll discuss ensemble learning, its importance, use cases, and techniques.
What Is Ensemble Learning?
In machine learning and statistics, “ensemble” refers to methods generating various hypotheses while using a common base learner.
And ensemble learning is a machine learning approach where multiple models (like experts or classifiers) are strategically created and combined with the aim of solving a computational problem or making better predictions.
This approach seeks to improve the prediction, function approximation, classification, etc., performance of a given model. It’s also used to eliminate the possibility of you choosing a poor or less valuable model out of many. To achieve improved predictive performance, several learning algorithms are used.
Ensemble Learning in ML
In machine learning models, there are some sources like bias, variance, and noise that may cause errors. Ensemble learning can help reduce these error-causing sources and ensure the stability and accuracy of your ML algorithms.
Here are why ensemble learning is being used in various scenarios:
Choosing the Right Classifier
Ensemble learning helps you choose a better model or classifier while reducing the risk that may result due to poor model selection.
There are different types of classifiers used for different problems, such as support vector machines (SVM), multilayer perceptron (MLP), naive Bayes classifiers, decision trees, etc. In addition, there are different realizations of classification algorithms that you need to choose. The performance of different training data can be different as well.
But instead of selecting just one model, if you use an ensemble of all these models and combine their individual outputs, you may avoid selecting poorer models.
Many ML methods and models are not that effective in their results if you feed them inadequate data or a large volume of data.
On the other hand, ensemble learning can work in both scenarios, even if the data volume is too little or too much.
If there is inadequate data, you can use bootstrapping to train various classifiers with the help of different bootstrap data samples.
If there is a large data volume that can make the training of a single classifier challenging, then can strategically partition data into smaller subsets.
A single classifier might not be able to solve some highly complex problems. Their decision boundaries separating data of various classes might be highly complex. So, if you apply a linear classifier to a non-linear, complex boundary, it won’t be able to learn it.
However, upon properly combining an ensemble of suitable, linear classifiers, you can make it learn a given nonlinear boundary. The classifier will divide the data into many easy-to-learn and smaller partitions, and each classifier will learn just one simpler partition. Next, different classifiers will be combined to produce an approx. decision boundary.
In ensemble learning, a vote of confidence is assigned to a decision that a system has made. Suppose you have an ensemble of various classifiers trained on a given problem. If the majority of classifiers do agree with the decision made, its outcome can be thought of as an ensemble with a high-confidence decision.
On the other hand, if half of the classifiers don’t agree with the decision made, it’s said to be an ensemble with a low-confidence decision.
However, low or high confidence is not always the correct decision. But there is a high chance that a decision with high confidence will be correct if the ensemble is properly trained.
Accuracy with Data Fusion
Data collected from multiple sources, when combined strategically, can improve the accuracy of classification decisions. This accuracy is higher than the one made with the help of a single data source.
How Does Ensemble Learning Work?
Ensemble learning takes multiple mapping functions that different classifiers have learned and then combines them to create a single mapping function.
Here’s an example of how ensemble learning works.
Example: You are creating a food-based application for the end users. To offer a high-quality user experience, you want to collect their feedback regarding the problems they face, prominent loopholes, errors, bugs, etc.
For this, you can ask the opinions of your family, friends, co-workers, and other people with whom you communicate frequently regarding their food choices and their experience of ordering food online. You can also release your application in beta to collect real-time feedback with no bias or noise.
So, what you are actually doing here is considering multiple ideas and opinions from different people to help improve the user experience.
Ensemble learning and its models work in a similar way. It uses a set of models and combines them to produce a final output to improve prediction accuracy and performance.
Basic Ensemble Learning Techniques
A “mode” is a value appearing in a dataset. In ensemble learning, ML professionals use multiple models to create predictions about every data point. These predictions are considered individual votes and the prediction that most models have made is considered the final prediction. It’s mostly used in classification problems.
Example: Four people rated your application 4 while one of them rated it 3, then the mode would be 4 since the majority voted 4.
Using this technique, professionals take into account all the model predictions and calculate their average to come up with the final prediction. It’s mostly used in making predictions for regression problems, calculating probabilities in classification problems, and more.
Example: In the above example, where four people rated your app 4 while one person rated it 3, the average would be (4+4+4+4+3)/5=3.8
#3. Weighted Average
In this ensemble learning method, professionals allocate different weights to different models for making a prediction. Here, the allocated weight describes each model’s relevance.
Example: Suppose 5 individuals provided feedback on your application. Out of them, 3 are application developers, while 2 don’t have any app development experience. So, the feedback of those 3 people will be given more weightage than the rest 2.
Advanced Ensemble Learning Techniques
Bagging (Bootstrap AGGregatING) is a highly intuitive and simple ensemble learning technique with a good performance. As the name suggests, it’s made by combining two terms “Bootstrap” and “aggregation”.
Bootstrapping is another sampling method where you will need to create subsets of several observations taken from an original data set with replacement. Here, the subset size will be the same as that of the original data set.
So, in bagging, subsets or bags are used to understand the distribution of the complete set. However, the subsets could be smaller than the original data set in bagging. This method involves a single ML algorithm. The aim of combining different models’ results is to obtain a generalized outcome.
Here’s how bagging works:
Several subsets are generated from the original set and observations are selected with replacements. The subsets are used in the training of models or decision trees.
A weak or base model is created for each subset. The models will be independent of one another and run in parallel.
The final prediction will be made by combining each prediction from every model using statistics like averaging, voting, etc.
Popular algorithms used in this ensemble technique are:
Bagged decision trees
The advantage of this method is that it helps keep variance errors to the minimum in decision trees.
In stacking or stacked generalization, predictions from different models, like a decision tree, are used to create a new model to make predictions on this test set.
Stacking involves the creation of bootstrapped subsets of data for training models, similar to bagging. But here, the output of models is taken as an input to be fed to another classifier, known as a meta-classifier for the final prediction of the samples.
The reason why two classifier layers are used is to determine if the training data sets are learned appropriately. Although the two-layered approach is common, more layers can also be used.
For instance, you can use 3-5 models in the first layer or level-1 and a single model in layer 2 or level 2. The latter will combine the predictions obtained in level 1 to make the final prediction.
Furthermore, you can use any ML learning model for aggregating predictions; a linear model like linear regression, logistic regression, etc., is common.
Popular ML algorithms used in stacking are:
Note: Blending uses a validation or holdout set from the training dataset for making predictions. Unlike stacking, blending involves predictions to be made only from the holdout.
Boosting is an iterative ensemble learning method that adjusts a specific observation’s weight depending on its last or previous classification. This means every subsequent model aims at correcting the errors found in the previous model.
If the observation is not classified correctly, then boosting increases the weight of the observation.
In boosting, professionals train the first algorithm for boosting on a complete dataset. Next, they build the subsequent ML algorithms by using the residuals extracted from the previous boosting algorithm. Thus, more weight is given to the incorrect observations predicted by the previous model.
Here’s how it works step-wise:
A subset will be generated out of the original data set. Every data point will have the same weights initially.
Creating a base model takes place on the subset.
The prediction will be made on the complete dataset.
Using the actual and predicted values, errors will be calculated.
Incorrectly predicted observations will be given more weights
A new model will be created and the final prediction will be made on this data set, while the model tries to correct the previously made errors. Multiple models will be created in a similar way, each correcting the previous errors
The final prediction will be made from the final model, which is the weighted mean of all the models.
Popular boosting algorithms are:
The benefit of boosting is that it generates superior predictions and reduces errors due to bias.
Other Ensemble Techniques
A mixture of Experts: it’s used to train multiple classifiers, and their outputs are ensemble with a general linear rule. Here, the weights given to the combinations are determined by a trainable model.
Majority voting: it involves choosing an odd classifier, and predictions are computed for each sample. The class receiving the maximum class out of a classifier pool will be the predicted class of the ensemble. It’s used for solving problems like binary classification.
Max Rule: it uses the probability distributions of each classifier and employs confidence in making predictions. It’s used for multi-class classification problems.
Use Cases of Ensemble Learning
#1. Face and emotion detection
Ensemble learning utilizes techniques like independent component analysis (ICA) to perform face detection.
Moreover, ensemble learning is used in detecting the emotion of a person through speech detection. In addition, its capabilities help users perform facial emotion detection.
Fraud detection: Ensemble learning helps enhance the power of normal behavior modeling. This is why it’s deemed to be efficient in detecting fraudulent activities, for instance, in credit card and banking systems, telecommunication fraud, money laundering, etc.
DDoS: Distributed denial of service (DDoS) is a deadly attack on an ISP. Ensemble classifiers can reduce error detection and also discriminate attacks from genuine traffic.
Intrusion detection: Ensemble learning can be used in monitoring systems like intrusion detection tools to detect intruder codes by monitoring networks or systems, finding anomalies, and so on.
Detecting malware: Ensemble learning is quite effective in detecting and classifying malware code like computer viruses and worms, ransomware, trojan horses, spyware, etc. using machine learning techniques.
#3. Incremental Learning
In incremental learning, an ML algorithm learns from a new dataset while retaining previous learnings but without accessing previous data that it has seen. Ensemble systems are used in incremental learning by making it learn an added classifier on every dataset as it becomes available.
Ensemble classifiers are useful in the field of medical diagnosis, such as the detection of neuro-cognitive disorders (like Alzheimer’s). It performs detection by taking MRI datasets as inputs and classifying cervical cytology. Apart from that, it’s applied in proteomics (study of proteins), neuroscience, and other areas.
#5. Remote Sensing
Change detection: Ensemble classifiers are used to perform change detection through methods like Bayesian average and majority voting.
Mapping land cover: Ensemble learning methods like boosting, decision trees, kernel principal component analysis (KPCA), etc. are being used to detect and map land cover efficiently.
Accuracy is a critical aspect of finance, whether it’s calculation or prediction. It highly influences the output of the decisions you make. These can also analyze changes in stock market data, detect manipulation in stock prices, and more.
Additional Learning Resources
#1. Ensemble Methods for Machine Learning
This book will help you learn and implement important methods of ensemble learning from scratch.
I hope you now have some idea about ensemble learning, its methods, use cases, and why using it can be beneficial for your use case. It has the potential to solve many real-life challenges, from the domain of security and app development to finance, medicine, and more. Its uses are expanding, so there is likely to be more improvement in this concept in the near future.
Amrita is a freelance copywriter and content writer. She helps brands enhance their online presence by creating awesome content that connects and converts. She has completed her Bachelor of Technology (B.Tech) in Aeronautical Engineering…. read more