Is there any way to classify a data that looks like this with 10 different classes? is there any preprocessing required to make the data more separable to enhance the classification accuracyenter image description here?
SVM and logistic regression can deal better with multi-classification.
Data Recommendation:
(If I am dealing with your data: I don't see there is much evidence to classify them into 10 categories, would recommend grouping the categories of the dependent variable into three or four at least that might help to a user instead of the wrong classification )
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I'm approaching a 4 class classification problem, it's not particularly unbalanced, no missing features a lot of observation.. It seems everything good but when I approach the classification with fitcecoc it classifies everything as part of the first class. I try. to use fitclinear and fitcsvm on one vs all decomposed data but gaining the same results. Do you have any clue about the reason of that problem ?
Here are a few recommendations:
Have you normalized your data? SVM is sensitive to the features being
from different scales.
Save the mean and std you obtain during the training and use
those values during the prediction phase for normalizing the test
samples.
Change the C value and see if that changes the results.
I hope these help.
Would it be accurate to include an expert system in an image classifying application? (I am working with Matlab, have some experience with image processing and no experience with expert systems.)
What I'm planning on doing is adding an extra feature vector that is actually an answer to a question. Is this fine?
For example: Assume I have two questions that I want the answers to : Question 1 and Question 2. Knowing the answers to these 2 questions should help classify the test image more accurately. I understand expert systems are coded differently from an image classifier but my question is would it be wrong to include the answers to these 2 questions, in a numerical form (1 can be yes, and 0 can be no) and pass this information along with the other feature vectors into a classifier.
If it matters, my current classifier is an SVM.
Regarding training images: yes, they too will be trained with the 2 extra feature vectors.
Converting a set of comments to an answer:
A similar question in cross-validated already explains that it can be done as long as data is properly preprocessed.
In short: you can combine them as long as training (and testing) data is properly preprocessed (e.g. standardized). Standardization improves the performance of most linear classifiers because it scales the variables so they have the similar weight in the learning process and improves the numerical stability (and performance) when variables are sampled from gaussian-like distributions (which is achieved by standarization).
With that, if continuous variables are standardized and categorical variables are encoded as (-1, +1) the SVM should work well. Whether it will improve or not the performance of the classifier depends on the quality of those cathegorical variables.
Answering the other question in the comment.. while using kernel SVM with for example a chi square kernel, the rows of the training data are suppose to behave like histograms (all positive and usually l1-normalized) and therefore introducing a (-1, +1) feature breaks the kernel. Using a RBF kernel the rows of the data are suppose to be L2 normalized, and again, introducing (-1, +1) features might introduce unexpected behaviour (I'm not very sure what exactly the effect would be..).
I worked on similar problem. if multiple features can be extracted from your images then you can train different classifier by using different features. You can think about these classifiers as experts in answering questions based on the features they used in training. Instead of using labels as outputs, it is better to use confidence values. uncertainty can be very important in this manner. you can use these experts to generate values. these values can be combined and used to train another classifier.
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.
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
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