I have a medical image dataset of ~10K 256x256 images with which I am training a deep neural classifier for disease classification. I have been working with popular CNNs like InceptionV3 and ResNets.
These models have achieved validation set accuracies in the 50-60% range and I noticed that they were overfitting. So to improve the performance, I then tried common strategies like a dropout in the dense layers, smaller learning rates, and L2 regularization. After these modifications showed no reduction in overfitting, I next moved to smaller and simpler architectures with just 2-3 convolution layers + 1 FC classification layer which I thought would mitigate the issue. However, with the simpler models, the learning curves still showed signs of overfitting. Particularly, when training for 100 epochs, the models would have similar train and validation losses for the first 20-30 epochs, but then diverge after that.
I'm not sure what other strategies I can experiment with at this point and I'm worried that trying more experiments aimlessly is inefficient. Should I just accept that the models cannot generalize to this task well?
Additionally, FYI, the dataset is imbalanced, but I have dealt with this using data augmentation and a weighted cross-entropy loss as well but no real difference.
Try to use modern classification approaches like transformers or efficientnets - their accuracy is higher. To compare different modern architectures please use paperswithcode.
Augmentations, regularizations are must-have in training process, doesn't matter if balanced or imbalanced data you have.
You can try to make over- or undersampling of your data to get better results
Try to use warmup and learning rate schedules, this improves the convergence of the model
I am new to ML and going through that i found difficulty in choosing the optimizer there are lots of optimizer, but I am confused when to use the Stochastic Gradient Descent optimizer and Why?
Here is my understanding: the modern, sophisticated gradient descent algorithms like ADAM use more computational power than SGD. They make trial moves before committing to a step down the gradient. This extra sophistication is quite helpful when working with deeper neural network architectures. Such architectures may have very complex loss hypersurfaces, with multiple saddle points and local minima. ADAM and its relatives do a good job finding the solution in these situations.
If your model architectures are simpler, apparently that the extra effort is not worth it. Stochastic gradient descent works well for simple models, and if you add Nesterov momentum and weight decay, you can often converge on a solution faster than you can with ADAM.
Another place that SGD is used is as a substitute for solving very large linear regressions by ordinary least-squares. In theory, any invertible matrix can be solved exactly. But it's entirely possible in the modern age to have a matrix inversion problem whose intermediate terms require more memory than you have in RAM. In that case, SGD is the more practical way to solve the problem.
I am trying to use machine learning to predict a dataset. It is a regression problem with 180 input features and 1 continuously-valued output. I try to compare deep neural networks, random forest regression, and linear regression.
As I expect, 3-hidden-layer deep neural networks outperform other two approaches with a root mean square error (RMSE) of 0.1. However, I unexpected to see that random forest even performs worse than linear regression (RMSE 0.29 vs. 0.27). In my expectation, the random forest can discover more complex dependencies between features to decrease error. I have tried to tune the parameters of random forest (number of trees, maximum features, max_depth, etc.). I also tried different K-cross validation, but the performance is still less than linear regression.
I searched online, and one answer says linear regression may perform better if features have a smooth, nearly linear dependence on the covariates. I do not fully get the point because if that is the case, should not deep neural networks give much performance gain?
I am struggling to give an explanation. Under what situation, random forest is worse than linear regression, but deep neural networks can perform much better?
If your features explain linear relation to the target variable then a Linear Model usually performs well than a Random Forest Model. It totally depends on the linear relations between your features.
That said, Linear models are not superior or the Random Forest is any inferior one.
Try scaling and transforming the data using MinMaxScaler() from scikit-learn to see if the linear model improves further
Pro Tips
If linear model is working like a charm you need to ask your self Why? and How? And get into the basics of both the models to understand why it worked on your data. These questions will lead you to feature engineer better. And as a matter of fact, Kaggle Grand Masters do use Linear Models in stacking to get that top 1% score by capturing the linear relations in the dataset.
So at the end of the day, linear models could wonders too.
Most classification algorithms are developed to improve the training speed. However, is there any classifier or algorithm focusing on the decision making speed(low computation complexity and simple realizable structure)? I can get enough training dataļ¼and endure the long training time.
There are many methods which classify fast, you could more or less sort models by classification speed in a following way (first ones - the fastest, last- slowest)
Decision Tree (especially with limited depth)
Linear models (linear regression, logistic regression, linear svm, lda, ...) and Naive Bayes
Non-linear models based on explicit data transformation (Nystroem kernel approximation, RVFL, RBFNN, EEM), Kernel methods (such as kernel SVM) and shallow neural networks
Random Forest and other committees
Big Neural Networks (ie. CNN)
KNN with arbitrary distance
Obviously this list is not exhaustive, it just shows some general ideas.
One way of obtaining such model is to build a complex, slow model, then use it as a black box label generator to train a simplier model (but on potentialy infinite training set) - thus getting a fast classifier at the cost of very expensive training. There are many works showing that one can do that for example by training a shallow neural network on outputs of deep nn.
In general classification speed should not be a problem. Some exceptions are algorithms which have a time complexity depending on the number of samples you have for training. One example is k-Nearest-Neighbors which has no training time, but for classification it needs to check all points (if implemented in a naive way). Other examples are all classifiers which work with kernels since they compute the kernel between the current sample and all training samples.
Many classifiers work with a scalar product of the features and a learned coefficient vector. These should be fast enough in almost all cases. Examples are: Logistic regression, linear SVM, perceptrons and many more. See #lejlot's answer for a nice list.
If these are still too slow you might try to reduce the dimension of your feature space first and then try again (this also speeds up training time).
Btw, this question might not be suited for StackOverflow as it is quite broad and recommendation instead of problem oriented. Maybe try https://stats.stackexchange.com/ next time.
I have a decision tree which is represented in the compressed form and which is at least 4 times faster than the actual tree in classifying an unseen instance.
I know SVMs are supposedly 'ANN killers' in that they automatically select representation complexity and find a global optimum (see here for some SVM praising quotes).
But here is where I'm unclear -- do all of these claims of superiority hold for just the case of a 2 class decision problem or do they go further? (I assume they hold for non-linearly separable classes or else no-one would care)
So a sample of some of the cases I'd like to be cleared up:
Are SVMs better than ANNs with many classes?
in an online setting?
What about in a semi-supervised case like reinforcement learning?
Is there a better unsupervised version of SVMs?
I don't expect someone to answer all of these lil' subquestions, but rather to give some general bounds for when SVMs are better than the common ANN equivalents (e.g. FFBP, recurrent BP, Boltzmann machines, SOMs, etc.) in practice, and preferably, in theory as well.
Are SVMs better than ANN with many classes? You are probably referring to the fact that SVMs are in essence, either either one-class or two-class classifiers. Indeed they are and there's no way to modify a SVM algorithm to classify more than two classes.
The fundamental feature of a SVM is the separating maximum-margin hyperplane whose position is determined by maximizing its distance from the support vectors. And yet SVMs are routinely used for multi-class classification, which is accomplished with a processing wrapper around multiple SVM classifiers that work in a "one against many" pattern--i.e., the training data is shown to the first SVM which classifies those instances as "Class I" or "not Class I". The data in the second class, is then shown to a second SVM which classifies this data as "Class II" or "not Class II", and so on. In practice, this works quite well. So as you would expect, the superior resolution of SVMs compared to other classifiers is not limited to two-class data.
As far as i can tell, the studies reported in the literature confirm this, e.g., In the provocatively titled paper Sex with Support Vector Machines substantially better resolution for sex identification (Male/Female) in 12-square pixel images, was reported for SVM compared with that of a group of traditional linear classifiers; SVM also outperformed RBF NN, as well as large ensemble RBF NN). But there seem to be plenty of similar evidence for the superior performance of SVM in multi-class problems: e.g., SVM outperformed NN in protein-fold recognition, and in time-series forecasting.
My impression from reading this literature over the past decade or so, is that the majority of the carefully designed studies--by persons skilled at configuring and using both techniques, and using data sufficiently resistant to classification to provoke some meaningful difference in resolution--report the superior performance of SVM relative to NN. But as your Question suggests, that performance delta seems to be, to a degree, domain specific.
For instance, NN outperformed SVM in a comparative study of author identification from texts in Arabic script; In a study comparing credit rating prediction, there was no discernible difference in resolution by the two classifiers; a similar result was reported in a study of high-energy particle classification.
I have read, from more than one source in the academic literature, that SVM outperforms NN as the size of the training data decreases.
Finally, the extent to which one can generalize from the results of these comparative studies is probably quite limited. For instance, in one study comparing the accuracy of SVM and NN in time series forecasting, the investigators reported that SVM did indeed outperform a conventional (back-propagating over layered nodes) NN but performance of the SVM was about the same as that of an RBF (radial basis function) NN.
[Are SVMs better than ANN] In an Online setting? SVMs are not used in an online setting (i.e., incremental training). The essence of SVMs is the separating hyperplane whose position is determined by a small number of support vectors. So even a single additional data point could in principle significantly influence the position of this hyperplane.
What about in a semi-supervised case like reinforcement learning? Until the OP's comment to this answer, i was not aware of either Neural Networks or SVMs used in this way--but they are.
The most widely used- semi-supervised variant of SVM is named Transductive SVM (TSVM), first mentioned by Vladimir Vapnick (the same guy who discovered/invented conventional SVM). I know almost nothing about this technique other than what's it is called and that is follows the principles of transduction (roughly lateral reasoning--i.e., reasoning from training data to test data). Apparently TSV is a preferred technique in the field of text classification.
Is there a better unsupervised version of SVMs? I don't believe SVMs are suitable for unsupervised learning. Separation is based on the position of the maximum-margin hyperplane determined by support vectors. This could easily be my own limited understanding, but i don't see how that would happen if those support vectors were unlabeled (i.e., if you didn't know before-hand what you were trying to separate). One crucial use case of unsupervised algorithms is when you don't have labeled data or you do and it's badly unbalanced. E.g., online fraud; here you might have in your training data, only a few data points labeled as "fraudulent accounts" (and usually with questionable accuracy) versus the remaining >99% labeled "not fraud." In this scenario, a one-class classifier, a typical configuration for SVMs, is the a good option. In particular, the training data consists of instances labeled "not fraud" and "unk" (or some other label to indicate they are not in the class)--in other words, "inside the decision boundary" and "outside the decision boundary."
I wanted to conclude by mentioning that, 20 years after their "discovery", the SVM is a firmly entrenched member in the ML library. And indeed, the consistently superior resolution compared with other state-of-the-art classifiers is well documented.
Their pedigree is both a function of their superior performance documented in numerous rigorously controlled studies as well as their conceptual elegance. W/r/t the latter point, consider that multi-layer perceptrons (MLP), though they are often excellent classifiers, are driven by a numerical optimization routine, which in practice rarely finds the global minimum; moreover, that solution has no conceptual significance. On the other hand, the numerical optimization at the heart of building an SVM classifier does in fact find the global minimum. What's more that solution is the actual decision boundary.
Still, i think SVM reputation has declined a little during the past few years.
The primary reason i suspect is the NetFlix competition. NetFlix emphasized the resolving power of fundamental techniques of matrix decomposition and even more significantly t*he power of combining classifiers. People combined classifiers long before NetFlix, but more as a contingent technique than as an attribute of classifier design. Moreover, many of the techniques for combining classifiers are extraordinarily simple to understand and also to implement. By contrast, SVMs are not only very difficult to code (in my opinion, by far the most difficult ML algorithm to implement in code) but also difficult to configure and implement as a pre-compiled library--e.g., a kernel must be selected, the results are very sensitive to how the data is re-scaled/normalized, etc.
I loved Doug's answer. I would like to add two comments.
1) Vladimir Vapnick also co-invented the VC dimension which is important in learning theory.
2) I think that SVMs were the best overall classifiers from 2000 to 2009, but after 2009, I am not sure. I think that neural nets have improved very significantly recently due to the work in Deep Learning and Sparse Denoising Auto-Encoders. I thought I saw a number of benchmarks where they outperformed SVMs. See, for example, slide 31 of
http://deeplearningworkshopnips2010.files.wordpress.com/2010/09/nips10-workshop-tutorial-final.pdf
A few of my friends have been using the sparse auto encoder technique. The neural nets build with that technique significantly outperformed the older back propagation neural networks. I will try to post some experimental results at artent.net if I get some time.
I'd expect SVM's to be better when you have good features to start with. IE, your features succinctly capture all the necessary information. You can see if your features are good if instances of the same class "clump together" in the feature space. Then SVM with Euclidian kernel should do the trick. Essentially you can view SVM as a supercharged nearest neighbor classifier, so whenever NN does well, SVM should do even better, by adding automatic quality control over the examples in your set. On the converse -- if it's a dataset where nearest neighbor (in feature space) is expected to do badly, SVM will do badly as well.
- Is there a better unsupervised version of SVMs?
Just answering only this question here. Unsupervised learning can be done by so-called one-class support vector machines. Again, similar to normal SVMs, there is an element that promotes sparsity. In normal SVMs only a few points are considered important, the support vectors. In one-class SVMs again only a few points can be used to either:
"separate" a dataset as far from the origin as possible, or
define a radius as small as possible.
The advantages of normal SVMs carry over to this case. Compared to density estimation only a few points need to be considered. The disadvantages carry over as well.
Are SVMs better than ANNs with many classes?
SVMs have been designated for discrete classification. Before moving to ANNs, try ensemble methods like Random Forest , Gradient Boosting, Gaussian Probability Classification etc
What about in a semi-supervised case like reinforcement learning?
Deep Q learning provides better alternatives.
Is there a better unsupervised version of SVMs?
SVM is not suited for unsupervised learning. You have other alternatives for unsupervised learning : K-Means, Hierarchical clustering, TSNE clustering etc
From ANN perspective, you can try Autoencoder, General adversarial network
Few more useful links:
towardsdatascience
wikipedia