Ensemble Data Mining means using ensemble methods to make progress or get specific results. The ensemble method was not introduced in Machine Learning until about fifteen years ago. Traditional methods of Machine Learning have been known to give single results at a time, hence the need for Ensemble Data Mining methods came up.
This article helps find and bring ensemble data mining in straightforward terms. Read on to find out more!
Table of Contents
What is Data Mining?
Data Mining refers to the process of analyzing bulky datasets for relationships and patterns that can prove helpful in solving problems in business. These processes, methods, and techniques help businesses predict coming trends and make well-informed decisions regarding business strategies and plans.
Data Mining is a fundamental discipline (among many) in Data Science and a vital part of Data Analytics. Data Mining is a step in KDD, also known as Knowledge Discovery in Databases (KDD is a methodology in Data Science for gathering, processing, and analyzing data).
Until 1990, the term “Data Mining” did not exist. Companies at that time were just coming to terms with how important data was to the growth of their businesses and services. Also, Data Mining has roots in three related science disciplines: Artificial Intelligence (human-like intelligence displayed by machines and software), Machine Learning (algorithms that gather knowledge to predict scenarios and behavior), and Statistics (the relationship between numbers and data).
So far, year by year, technology in this field keeps updating itself, as companies and individuals are testing new waters and breaking new grounds in the potential to be accrued from merging computing power and big data.
What is Ensemble Data Mining?
Ensemble Data Mining is the name given to Machine Learning methods and techniques where several dissimilar models are merged to produce a single optimal result in Data Mining, either by using Diverse datasets or various algorithms.
The drive for using the Ensemble Approach is to lessen the prediction error. However, the base models must be different for the prediction error for this approach to work.
In creating a prediction, this technique follows the idea of seeking results from multiple sources of advice. Although the ensemble model has numerous base models, it functions, performs, and releases output as a single model.
However, note that Ensemble approaches are used in most fundamental Data Science applications, not just in Data Mining.
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Why should you use the Ensemble Approach in Data Mining?
The fundamental challenge is not obtaining very accurate base models but rather getting base models with various flaws. For example, if ensembles are employed for classification, high accuracies can be achieved even if multiple basis models misclassify different training samples.
This way, if different results are taken into account and used to get a particular outcome, then it can be said that all sample sizes have gone through various processes, and many factors have been accounted for.
Types of Ensemble Data Mining Methods
Ensemble Data Mining methods use the strength of several models to produce better Prediction Accuracy than any of the individual models could on their own.
When constructing an ensemble, the primary purpose is the same as when forming a committee of people. Each committee member should be as competent as possible, but the members should complement one another.
If the members are not complementary (if they constantly agree), then the committee is unnecessary—anyone can do the job. If the members are complementary, the chances are that if one or more of them makes a mistake, the other members will be able to rectify it.
Generally, there are three ensemble methods: Bagging, Random Forest Models, and Boosting:
Bagging gets its name because it combines Bootstrapping and Aggregation to form one Ensemble model. Given a sample of data, different bootstrapped subsamples are extracted. A Decision Tree is created on each of the bootstrapped subsamples. After each subsample’s Decision Tree has been formed, an algorithm is used to aggregate the decision trees to develop the most efficient predictor. The image below explains this:
2) Random Forest Models
Random Forest Models are very identical to Bagging though they work in different ways. When deciding how to make decisions, the Decision Trees in the Bagging method have the full complement of features to select from. While the bootstrapped samples might appear different, one similar characteristic will be that the data will change similarity at the same features throughout each model.
On the other hand, Random Forest models choose where to change similarities based on randomly selected features. Random Forest models induce a level of variation because each tree will split based on random features. This offers a greater level of results which can draw much more different results and produce a more accurate prediction.
The image below provides a graphic understanding.
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The Boosting Method comprises the use of algorithms called Strong and Weak Learners. AdaBoost (which stands for Adaptive Boosting) is the most used of all Boosting Algorithms, where the main model is built on several weak learners. Weak learners are so-called because they are characteristically simple with restricted prediction abilities and, as a result, are just slightly better at accuracy than random guesses. However, unlike Bagging, Boosting is a sequential method and cannot be used for parallel operations.
The adaptation capability of AdaBoost was a significant factor in this technique, becoming one of the earliest successful Binary Classifiers. Sequential Decision Trees were the core of such adaptability, where each tree adjusted its weights based on prior knowledge of accuracies.
What are the Applications of Ensemble Data Mining?
Over the last decade, developments in processing power and speed have enabled us to shift away from manual, time-consuming, and labour-intensive processes and towards quick, easy, and automated data analysis. The more complex the datasets acquired, the greater the possibility of uncovering valuable insights.
Retailers, banks, manufacturers, telecommunications providers, and insurers, among others, now use data mining to discover relationships between and among everything from price optimization, promotions, and demographics to how the economy, risk, competition, and social media are affecting their business models, revenues, operations, and customer relationships.
Recently, theoretical and experimental developments have led to several ensemble methods, significantly Boosting and Bagging, being used to solve many real problems. However, these ensemble methods also appear to be applicable to current and upcoming issues of Distributed Data Mining and online applications. The world of ensemble data mining is just starting for those who do believe.
Ensemble methods began as a separate area within Machine Learning and were largely encouraged by the thought of leveraging the power of multiple models instead of just trusting one model built on a single training dataset.
Although Ensemble Approaches can help develop complicated algorithms and provide high-accuracy results, they are largely ignored in businesses where interpretability is more crucial. Nonetheless, the efficacy of these methods is apparent, and their advantages, when applied in the proper industries, can be enormous. In domains such as healthcare, even minor improvements in the accuracy of Machine Learning algorithms can be highly beneficial.
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