i hope everyone is doing well
I need some help with generative models.
So im working on a project where the main task is to build a binary classification model. In the dataset which contains 300000 sample and 100 feature, there is an imbalance between the 2 classes where majority class is too much bigger than the minory class.
To handle this problem, i'm using VAE (variational autoencoders) to solve this problem.
So i started training the VAE on the minority class and then use the decoder part of the VAE to generate new or fake samples that are similars to the minority class then concatenate this new data with training set in order to have a new balanced training set.
My question is : is there anyway to evalutate generative models like vae, like is there a way to know if the data generated is similar to the real one ??
I have read that there is some metrics to evaluate generated data like inception distance and Frechet inception distance but i saw that they have been only used on image data
I wanna know if i can use them too on my dataset ?
Thanks in advance
I believe your data is not image as you say there are 100 features. What I believe that you can check the similarity between the synthesised features and the original features (the ones belong to minority class), and keep only the ones with certain similarity. Cosine similarity index would be useful for this problem.
That would be also very nice to check a scatter plot of the synthesised features with the original ones to see if they are close to each other. tSNE would be useful at this point.
Related
I have a binary classification problem I'm trying to tackle in Keras. To start, I was following the usual MNIST example, using softmax as the activation function in my output layer.
However, in my problem, the 2 classes are highly unbalanced (1 appears ~10 times more often than the other). And what's even more critical, they are non-symmetrical in the way they may be mistaken.
Mistaking an A for a B is way less severe than mistaking a B for an A. Just like a caveman trying to classify animals into pets and predators: mistaking a pet for a predator is no big deal, but the other way round will be lethal.
So my question is: how would I model something like this with Keras?
thanks a lot
A non-exhaustive list of things you could do:
Generate a balanced data set using data augmentations. If the data are images, you can add image augmentations in a custom data generator that will output balanced amounts of data from each class per batch and save the results to a new data set. If the data are tabular, you can use a library like imbalanced-learn to perform over/under sampling.
As #Daniel said you can use class_weights during training (in the fit method) in a way that mistakes on important class are penalized more. See this tutorial: Classification on imbalanced data. The same idea can be implemented with a custom loss function with/without class_weights during training.
So say for each of my ‘things’ to classify I have:
{house, flat, bungalow, electricityHeated, gasHeated, ... }
Which would be made into a feature vector:
{1,0,0,1,0,...} which would mean a house that is heated by electricity.
For my training data I would have all this data- but for the actual thing I want to classify I might only have what kind of house it is, and a couple other things- not all the data ie.
{1,0,0,?,?,...}
So how would I represent this?
I would want to find the probability that a new item would be gasHeated.
I would be using a SVM linear classifier- I don’t have any core to show because this is purely theoretical at the moment. Any help would be appreciated :)
When I read this question, it seems that you may have confused with feature and label.
You said that you want to predict whether a new item is "gasHeated", then "gasHeated" should be a label rather than a feature.
btw, one of the most-common ways to deal with missing value is to set it as "zero" (or some unused value, say -1). But normally, you should have missing value in both training data and testing data to make this trick be effective. If this only happened in your testing data but not in your training data, it means that your training data and testing data are not from the same distribution, which basically violated the basic assumption of machine learning.
Let's say you have a trained model and a testing sample {?,0,0,0}. Then you can create two new testing samples, {1,0,0,0}, {0,0,0,0}, and you will have two predictions.
I personally don't think SVM is a good approach if you have missing values in your testing dataset. Just like I have mentioned above, although you can get two new predictions, but what if each one has different predictions? It is difficult to assign a probability to results of SVM in my opinion unless you use logistic regression or Naive Bayes. I would prefer Random Forest in this situation.
I am currently trying to use satellite imagery to recognize Apples orchards. And I am facing a small problem in the number of representative data for each class.
In fact my question is :
Is it possible to take randomly some different images in my "not-apples" class at each epoch because I have much more of theses (compared to the "apples" one) and I want to increase the probability my network will classify out an image unrepresentative.
Thanks in advance for your help
That is not possible in Keras. Keras will, by default, shuffle your training data and then train on it in a mini-batch fashion. However, there are still ways to re-balance your dataset.
The imbalanced training data problem that you are facing is pretty common. You have many options available to you; I list a few below:
You can adjust the relative weights of your classes using class_weight keyword of the model.fit() function.
You can "up-sample" your "apples" class or "down-sample" your "non-apples" class to have equal numbers of both classes during training.
You can generate synthetic images of your "apples" class to augment your data set. To this end, the ImageDataGenerator class in Keras can be particularly useful. This Keras tutorial is a good introduction to its usage.
In my experience, I've found #2 and #3 to be most useful. #1 is limited by the fact that the convergence of stochastic gradient descent suffers when using class weights differing by a couple orders of magnitude and smaller batch sizes.
Jason Brownlee has put together a list of tactics for dealing with imbalanced classes that might also be useful to you.
I am a deep-learning newbie and working on creating a vehicle classifier for images using Caffe and have a 3-part question:
Are there any best practices in organizing classes for training a
CNN? i.e. number of classes and number of samples for each class?
For example, would I be better off this way:
(a) Vehicles - Car-Sedans/Car-Hatchback/Car-SUV/Truck-18-wheeler/.... (note this could mean several thousand classes), or
(b) have a higher level
model that classifies between car/truck/2-wheeler and so on...
and if car type then query the Car Model to get the car type
(sedan/hatchback etc)
How many training images per class is a typical best practice? I know there are several other variables that affect the accuracy of
the CNN, but what rough number is good to shoot for in each class?
Should it be a function of the number of classes in the model? For
example, if I have many classes in my model, should I provide more
samples per class?
How do we ensure we are not overfitting to class? Is there way to measure heterogeneity in training samples for a class?
Thanks in advance.
Well, the first choice that you mentioned corresponds to a very challenging task in computer vision community: fine-grained image classification, where you want to classify the subordinates of a base class, say Car! To get more info on this, you may see this paper.
According to the literature on image classification, classifying the high-level classes such as car/trucks would be much simpler for CNNs to learn since there may exist more discriminative features. I suggest to follow the second approach, that is classifying all types of cars vs. truck and so on.
Number of training samples is mainly proportional to the number of parameters, that is if you want to train a shallow model, much less samples are required. That also depends on your decision to fine-tune a pre-trained model or train a network from scratch. When sufficient samples are not available, you have to fine-tune a model on your task.
Wrestling with over-fitting has been always a problematic issue in machine learning and even CNNs are not free of them. Within the literature, some practical suggestions have been introduced to reduce the occurrence of over-fitting such as dropout layers and data-augmentation procedures.
May not included in your questions, but it seems that you should follow the fine-tuning procedure, that is initializing the network with pre-computed weights of a model on another task (say ILSVRC 201X) and adapt the weights according to your new task. This procedure is known as transfer learning (and sometimes domain adaptation) in community.
I want to classify the news article into the category it belongs to. I have 4 categories of news eg." Technology,Sports,Politics and Health." And i have collected around 50 documents for each category as a Training Set
**Is the Training data enough for classification ??? And Which Algorithm should i use for classification?? SVM, Random Forest,Knn, ??
I am using Scikit-learn http://scikit-learn.org/ [python] library for my task
Thanks
There are many ways to attack this problem form CRFs to Random Forests.
With your limited training data, I would suggest going with a model with high bias such as the linear SVM. Start with training one vs all models for each class and predicting the class with the highest probably. This will give you a baseline for how hard your problem is with the given training data.
I prefer you to use Naive-Bayes classification. There is a tool called Ling-pipe where this is already implemented. What you want to do is just refer
http://alias-i.com/lingpipe/demos/tutorial/classify/read-me.html
There you have a small sample program Classifynews.java. Run that program by training the data and apply testing .A training data sample is given as "20 newsgroups"
http://qwone.com/~jason/20Newsgroups/
Training can be applied by training the data and if needed you can build an intermediate model and then apply the test data into that model. Naive-Bayes is good for the cases where training data is small.
But its accuracy increases as the size of training data increases. So try to include more news groups. Good luck. Try this and let me know