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15 votes
Accepted

How can an ensemble be more accurate than the best base classifier in that ensemble?

Lets consider binary classification. Imagine you have an ensemble made up of $K$ models. Assume each model has exactly $51\%$ accuracy. Further assume each model's error is uncorrelated with each ...
chessprogrammer's user avatar
6 votes
Accepted

Do deep learning algorithms represent ensemble-based methods?

You should think of them as different approaches. A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models. The primary connection between the ...
Matthew Gray's user avatar
  • 4,272
3 votes

Would a pipeline of different models be considered Ensemble Learning?

No. Ensemble Learning (EL) is a way to improve the model performance, which usually means to reduce bias (i.e., get a better model class) or reduce variance (i.e., get better at generalizing across ...
Luca Anzalone's user avatar
3 votes

Does it make sense to combine classifiers trained on the same dataset?

These are generally known as ensemble methods. Your method is essentially what Scikit-Learn's VotingClassifier does, which is perfectly reasonable and might give ...
htl's user avatar
  • 1,010
2 votes
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What ensemble methods are used in the state-of-the-art models?

In my opinion, it is not because ensemble methods are not good, just the state-of-the-art and Kaggle competitions are two different fields. Kaggle competitions can be understood as an industry project ...
CuCaRot's user avatar
  • 912
2 votes

Do deep learning algorithms represent ensemble-based methods?

Deep neural networks could - in principle - be a component of an ensemble of machine learning algorithms, yes. Ensemble method basically just means use multiple algorithms and combining their output ...
mindcrime's user avatar
  • 3,767
2 votes

Would a pipeline of different models be considered Ensemble Learning?

Would a system like this, which takes the output of one model and uses it as the input for another, be considered Ensemble Learning? Not usually. The main criteria to consider something as an ...
Neil Slater's user avatar
  • 32.6k
2 votes

How do I check that the combination of these models is good?

Goodness is subjective. Reliable knowledge isn't possible with that flimsy a quality objective. The sturdy objective criteria you gave is 95%, so it is bad by that criteria. (I'm assuming that the ...
Douglas Daseeco's user avatar
1 vote

Is there an approach where the output of one neural network is used to choose the next neural network?

Mixture of Experts might be what you are looking for. A Mixture of Experts model (MoE), divides a task into subtasks and designs seperate models for each of the tasks (This would be ...
DKDK's user avatar
  • 329
1 vote
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GAN with multiple discriminators

As Sadaf pointed out, MD-GAN is a well-known one. But it doesn't have multiple objectives as you wanted. One GAN architecture that has 2 discriminators, where both have different objectives, is ...
Robin van Hoorn's user avatar
1 vote

GAN with multiple discriminators

MD-GAN (multi-Discriminator Generative Adversarial Networks for Distributed Datasets ) would be among the ones that you might be looking for. It has been proposed a while ago now. This has been ...
Sadaf Shafi's user avatar
1 vote

Does it make sense to combine classifiers trained on the same dataset?

It is a simple way to do it but it is not wrong. If you are getting probabilities for each model, then, you can average them. Then, you can do the classification. Also, you assign weights to each ...
Abhishek Verma's user avatar
1 vote

When do the ensemble methods beat neural networks?

Speed: A classic random forest is O(n) to train and O(1) to run while a feedforward neural network is something like O(n^5) to train and O(n^4) to run, so for many tasks the CART ensemble can train ...
EngrStudent's user avatar
1 vote

How is an architecture composed of a second model that validates the first one called in machine learning?

Not in terms of models, but there is a terminology called 'Hierarchical learning', wherein if your model has a task to classify disease, then, If it detects a presence of a disease (disease/ no ...
Aniket Velhankar's user avatar
1 vote

Why does the error ensemble use the ceiling of the number of classifiers?

Y ensemble size voting wrong k = 50% or majority threshold If you have 11 models. Then the majority of models is anything bigger than 50% of the number of ensemble models. In the example where ...
Mr-Programs's user avatar
1 vote

Are there ensemble methods for regression?

? This means that there are not promising versions of this algorithm fro regression until 2012. After your question, I have found one of the survey research paper which is done or ensemple methods for ...
Sivaram Rasathurai's user avatar

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