I am working on a audio multi class classification problem (noise,vessels,2 types of animals) by using MFCC features. I am getting different results with different classifiers. I tried Bayesian type, Artificial Neural Networks, MSVM and decision trees.
Can anybody tell me what are the strengths and weaknesses of each of those 4 classifiers?
Many thanks
There is no “best” classifier
http://en.wikipedia.org/wiki/No_free_lunch_theorem
Averaged over all possible types of data distributions,
all classifi ers perform the same. Th us, we cannot say which algorithm
... is the “best”. Over any given data distribution or set of
data distributions, however, there is usually a best classifi er. Th us, when
faced with real data it’s a good idea to try many classifi ers. Consider
your purpose: Is it just to get the right score, or is it to interpret the
data? Do you seek fast computation, small memory requirements, or
confi dence bounds on the decisions? Diff erent classifi ers have diff erent
properties along these dimensions.
Learning OpenCV page 465
Related
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 used the neural network software for predicting the continous data. Obviously the prediction was better than the results obtained through regression analysis. Now i would like to derive a model expression from the trained weights obtained from the training of the continous data through the software, as suggested by many researchers on how to interpret the trained weights and biases for deriving the model equation i tried to derive one from the similar lines.
After deriving the equation i found that the equation was not able to replicate the same results as given by the neural network software. so i am exploring the new methods to derive the equation. I want to know where i am going wrong and if any one can provide me steps for deriving one it will be helpful.
I have read sometime ago about what you're talking about, but with some diferences. It would probably be useful to you. It's called 'knowledge distilling', if I remember well, and it is a way of extracting the knowledge inside the blackbox that a neural network is. It consists, roughly speaking, in training a simpler model that is easier to interpret, but preserving al the predictive power of the original neural network. I'm speaking from memory, so I'm sorry about the lack of detail. A search on Google will provide the exact references for it.
Hope to have helped.
I have a training dataset which gives me the ranking of various cricket players(2008) on the basis of their performance in the past years(2005-2007).
I've to develop a model using this data and then apply it on another dataset to predict the ranking of players(2012) using the data already given to me(2009-2011).
Which predictive modelling will be best for this? What are the pros and cons of using the different forms of regression or neural networks?
The type of model to use depends on different factors:
Amount of data: if you have very little data, you better opt for a simple prediction model like linear regression. If you use a prediction model which is too powerful you run into the risk of over-fitting your model with the effect that it generalizes bad on new data. Now you might ask, what is little data? That depends on the number of input dimensions and on the underlying distributions of your data.
Your experience with the model. Neural networks can be quite tricky to handle if you have little experience with them. There are quite a few parameters to be optimized, like the network layer structure, the number of iterations, the learning rate, the momentum term, just to mention a few. Linear prediction is a lot easier to handle with respect to this "meta-optimization"
A pragmatic approach for you, if you still cannot opt for one of the methods, would be to evaluate a couple of different prediction methods. You take some of your data where you already have target values (the 2008 data), split it into training and test data (take some 10% as test data, e.g.), train and test using cross-validation and compute the error rate by comparing the predicted values with the target values you already have.
One great book, which is also on the web, is Pattern recognition and machine learning by C. Bishop. It has a great introductory section on prediction models.
Which predictive modelling will be best for this? 2. What are the pros
and cons of using the different forms of regression or neural
networks?
"What is best" depends on the resources you have. Full Bayesian Networks (or k-Dependency Bayesian Networks) with information theoretically learned graphs, are the ultimate 'assumptionless' models, and often perform extremely well. Sophisticated Neural Networks can perform impressively well too. The problem with such models is that they can be very computationally expensive, so models that employ methods of approximation may be more appropriate. There are mathematical similarities connecting regression, neural networks and bayesian networks.
Regression is actually a simple form of Neural Networks with some additional assumptions about the data. Neural Networks can be constructed to make less assumptions about the data, but as Thomas789 points out at the cost of being considerably more difficult to understand (sometimes monumentally difficult to debug).
As a rule of thumb - the more assumptions and approximations in a model the easier it is to A: understand and B: find the computational power necessary, but potentially at the cost of performance or "overfitting" (this is when a model suits the training data well, but doesn't extrapolate to the general case).
Free online books:
http://www.inference.phy.cam.ac.uk/mackay/itila/
http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf
i have a training set and i want to use a classification method for classifying other documents according to my training set.my document types are news and categories are sports,politics,economic and so on.
i understand naive bayes and KNN completely but SVM and decision tree are vague and i dont know if i can implement this method by myself?or there is applications for using this methods?
what is the best method i can use for classifying docs in this way?
thanks!
Naive Bayes
Though this is the simplest algorithm and everything is deemed independent, in real text classification case, this method work great. And I would try this algorithm first for sure.
KNN
KNN is for clustering rather than classification. I think you misunderstand the conception of clustering and classification.
SVM
SVM has SVC(classification) and SVR(Regression) algorithms to do class classification and prediction. It sometime works good, but from my experiences, it has bad performance in text classification, as it has high demands for good tokenizers (filters). But the dictionary of the dataset always has dirty tokens. The accuracy is really bad.
Random Forest (decision tree)
I've never try this method for text classification. Because I think decision tree need several key nodes, while it's hard to find "several key tokens" for text classification, and random forest works bad for high sparse dimensions.
FYI
These are all from my experiences, but for your case, you have no better ways to decide which methods to use but to try every algorithm to fit your model.
Apache's Mahout is a great tool for machine learning algorithms. It integrates three aspects' algorithms: recommendation, clustering, and classification. You could try this library. But you have to learn some basic knowledge about Hadoop.
And for machine learning, weka is a software toolkit for experiences which integrates many algorithms.
Linear SVMs are one of the top algorithms for text classification problems (along with Logistic Regression). Decision Trees suffer badly in such high dimensional feature spaces.
The Pegasos algorithm is one of the simplest Linear SVM algorithms and is incredibly effective.
EDIT: Multinomial Naive bayes also works well on text data, though not usually as well as Linear SVMs. kNN can work okay, but its an already slow algorithm and doesn't ever top the accuracy charts on text problems.
If you are familiar with Python, you may consider NLTK and scikit-learn. The former is dedicated to NLP while the latter is a more comprehensive machine learning package (but it has a great inventory of text processing modules). Both are open source and have great community suport on SO.
I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.
But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?
1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.
ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.
In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.
If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.
If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.
If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)
You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.
Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)
aplly a seperate ANN for each category of features
for example
457 inputs 1 output for url terms ( ANN1 )
495 inputs 1 output for origurl ( ANN2 )
...
then train all of them
use another main ANN to join results