I am trying to detect the faces using the Matlab built-in viola jones face detection. Is there anyway that I can combine two classification models like "FrontalFaceCART" and "ProfileFace" into one in order to get a better result?
Thank you.
You can't combine models. That's a non-sense in any classification task since every classifier is different (works differently, i.e. different algorithm behind it, and maybe is also trained differently).
According to the classification model(s) help (which can be found here), your two classifiers work as follows:
FrontalFaceCART is a model composed of weak classifiers, based on classification and regression tree analysis
ProfileFace is composed of weak classifiers, based on a decision stump
More infos can be found in the link provided but you can easily see that their inner behaviour is rather different, so you can't mix them or combine them.
It's like (in Machine Learning) mixing a Support Vector Machine with a K-Nearest Neighbour: the first one uses separating hyperplanes whereas the latter is simply based on distance(s).
You can, however, train several models in parallel (e.g. independently) and choose the model that better suits you (e.g. smaller error rate/higher accuracy): so you basically create as many different classifiers as you like, give them the same training set, evaluate each accuracy (and/or other parameters) and choose the best model.
One option is to make a hierarchical classifier. So in a first step you use the frontal face classifier (assuming that most pictures are frontal faces). If the classifier fails, you try with the profile classifier.
I did that with a dataset of faces and it improved my overall classification accuracy. Furthermore, if you have some a priori information, you can use it. In my case the faces were usually in the middle up part of the picture.
To further improve your performance, without using the two classifiers in MATLAB you are using, you would need to change your technique (and probably your programming language). This is the best method so far: Facenet.
Related
I am a student and working on my first simple machine learning project. The project is about classifying articles into fake and true. I want to use SVM as classification algorithm and two different types of features:
TF-IDF
Lexical Features like the count of exclamation marks and numbers
I have figured out how to use the lexical features and TF-IDF as a features separately. However, I have not managed to figure out, how to combine them.
Is it possible, to train and test two separate learning algorithms (one with TF-IDF and the other one with lexical features) and later combine the results?
For example, can I calculate Accuracy, Precision and Recall for both separately and then take the average?
One way of combining two models is called model stacking. The idea behind it is, that you take the predictions of both models and feed them into a third model (called meta-model) which is then trained to make predictions given the output of the first two models. There is also another version of model stacking where you aditionally feed the original features into the meta-model.
However, in your case another way to combine both approaches would be to simply feed both the TF-IDF and the lexical features into one model and see how that performs.
For example, can I calculate Accuracy, Precision and Recall for both separately and then take the average?
This would unfortunately not work, because there is no combined model making those predictions for which your calculated metrics would be true.
I am looking for some comprehensive description. I couldn't find it via browsing as things are more clustered on the web and its not in my scope currently.
Classification and evolutionary computing is comparing oranges to apples. Let me explain:
Classification is a type of problem, where the goal is to determine a label given some input. (Typical example, given pixel values, determine image label).
Evolutionary computing is a family of algorithms to solve different types of problems. They work with a "population" of candidates (imagine a set of different neural networks trying to solve a given problem). Somehow you evaluate how good each candidate is in the given task (typically using a "fitness function", but there are other methods). Then a new generation of candidates is produced, taking the best candidates from the previous generation as a model, and including mutations and cross-over (that is, introducing changes). Repeat until happy.
Evolutionary computing can absolutely be used for classification! But there are examples where it is used in different ways. You may use evolutionary computing to create an artificial neural network controlling a robot (in this case, inputs are sensor values, outputs are commands for actuators). Or to create original content free of a given goal, as in Picbreeder.
Classification may be solved using evolutionary computation (maybe this is why you where confused in the first place) but other techniques are also common. You can use decision trees, or notably deep-learning (based on backpropagation).
Deep-learning based on backpropagation may sound similar to evolutionary computation, but it is quite different. Here you have only one artificial neural network, and a clear rule (backpropagation) telling you which changes to introduce every iteration.
Hope this helps to complement other answers!
Classification algorithms and evolutionary computing are different approaches. However, they are related in some ways.
Classification algorithms aim to identify the class label of new instances. They are trained with some labeled instances. For example, recognition of digits is a classification algorithm.
Evolutionary algorithms are used to find out the minimum or maximum solution of an optimization problem. They randomly explore the solution space of the given problem. They can find a good solution in a reasonable time and are not able to find the global optimum in all problems.
In some classification approaches, evolutionary algorithms are used to find out the optimal value of the parameters.
I'm in the overtures of designing a prose imitation system. It will read a bunch of prose, then mimic it. It's mostly for fun so the mimicking prose doesn't need to make too much sense, but I'd like to make it as good as I can, with a minimal amount of effort.
My first idea is to use my example prose to train a classifying feed-forward neural network, which classifies its input as either part of the training data or not part. Then I'd like to somehow invert the neural network, finding new random inputs that also get classified by the trained network as being part of the training data. The obvious and stupid way of doing this is to randomly generate word lists and only output the ones that get classified above a certain threshold, but I think there is a better way, using the network itself to limit the search to certain regions of the input space. For example, maybe you could start with a random vector and do gradient descent optimisation to find a local maximum around the random starting point. Is there a word for this kind of imitation process? What are some of the known methods?
How about Generative Adversarial Networks (GAN, Goodfellow 2014) and their more advanced siblings like Deep Convolutional Generative Adversarial Networks? There are plenty of proper research articles out there, and also more gentle introductions like this one on DCGAN and this on GAN. To quote the latter:
GANs are an interesting idea that were first introduced in 2014 by a
group of researchers at the University of Montreal lead by Ian
Goodfellow (now at OpenAI). The main idea behind a GAN is to have two
competing neural network models. One takes noise as input and
generates samples (and so is called the generator). The other model
(called the discriminator) receives samples from both the generator
and the training data, and has to be able to distinguish between the
two sources. These two networks play a continuous game, where the
generator is learning to produce more and more realistic samples, and
the discriminator is learning to get better and better at
distinguishing generated data from real data. These two networks are
trained simultaneously, and the hope is that the competition will
drive the generated samples to be indistinguishable from real data.
(DC)GAN should fit your task quite well.
I have a dataset with 20 features. 10 for age and 10 for weight. I want to classify the data for both separately then use the results from these 2 classifiers as an input to a third for the final result..
Is this possible with Weka????
Fusion of decisions is possible in WEKA (or with any two models), but not using the approach you describe.
Seeing as your using classifiers, each model will only output a class. You could use the two labels produced as features for a third model, but the lack of diversity in your inputs would most likely prevent the third model from giving you anything interesting.
At the most basic level, you could implement a voting scheme. Give each model a "vote" and then take assume that the correct class is the majority voted class. While this will give a rudimentary form of fusion, if you're familiar with voting theory you know that majority-rules somewhat falls apart when you have more than two classes.
I recommend that you use Combinatorial Fusion to fuse the output of the two classifiers. A good paper regarding the technique is available as a free PDF here. In essence, you use the Classifer::distributionForInstance() method provided by WEKA's classifiers and then use the sum of the distributions (called "scores") to rank the classes, choosing the class with the highest rank. The paper demonstrates that this method is superior to doing just voting alone.
I'm trying to combine multiple classifiers (ANN, SVM, kNN, ... etc.) using ensemble learning (viting, stacking ...etc.) .
In order to make a classifier, I'm using more than 20 types of explanatory variables.
However, each classifier has the best subset of explanatory variables. Thus, seeking the best combination of explanatory variables for each classifier in wrapper method,
I would like to combine multiple classifiers (ANN, SVM, kNN, ... etc.) using ensemble learning (viting, stacking ...etc.) .
By using the meta-learning with weka, I should be able to use the ensemble itself.
But I can not obtain the best combination of explanatory variables since wrapper method summarizes the prediction of each classifier.
I am not stick to weka if it can be solved easier in maybe matlab or R.
With ensemble approaches, best results have been achieved with very simple classifiers. Which on the other hand can be pretty fast, to make up for the ensemble cost.
This may seem counterintuitive at first: one would exepect a better input classifier to produce a better output. However, there are two reasons why this does not work.
First of all, with simple classifiers, you can usually tweak them more to get a diverse set of input classifiers. A full-dimensional method + feature bagging gives you a diverse set of classifiers. A classifier that internally does feature selection or reduction makes feature bagging largely disfunct for getting variety. Secondly, a complex method such as SVM is more likely to optimize/converge towards the very same result. After all, the complex methods are supposed to go through a much larger search space and find the best result in this search space. But that also means, you are more likely to get the same result again.
Last but not least, when using very primivite classifiers, the errors are better behaved and more likely to even out on ensemble combination.