Business case:
Forecasting fuel consumption at site.
Say fuel consumption C, is dependent on various factors x1,x2,...xn. So mathematically speaking, C = F{x1,x2,...xn}. I do not have any equation to put this.
I do have historical dataset from where I can get a correlation of C to x1,x2 .. etc. C,x1,x2,.. are all quantitative. Finding out the correlation seems tough for a person like me with limited statistical knowledge, for a n variable equation.
So, I was thinking of employing some supervised machine learning techniques for the same. I will train a classifier with the historic data to get a prediction for the next consumption.
Question: Am I thinking in the right way?
Question: If this is correct, my system should be an evolving one. So the more real data I am going to feed to the system, that would evolve my model to make a better prediction the next time. Is this a correct understanding?
If the above the statements are true, does the AdaptiveLogisticRegression algorithm, as present in Mahout, will be of help to me?
Requesting advises from the experts here!
Thanks in advance.
Ok, correlation is not a forecasting model. Correlation simply ascribes some relationship between the datasets based on covariance.
In order to develop a forecasting model, what you need to peform is regression.
The simplest form of regression is linear univariate, where C = F (x1). This can easily be done in Excel. However, you state that C is a function of several variables. For this, you can employ linear multivariate regression. There are standard packages that can perform this (within Excel for example), or you can use Matlab, etc.
Now, we are assuming that there is a "linear" relationship between C and the components of X (the input vector). If the relationship were not linear, then you would need more sophisticated methods (nonlinear regression), which may very well employ machine learning methods.
Finally, some series exhibit auto-correlation. If this is the case, then it may be possible for you to ignore the C = F(x1, x2, x3...xn) relationships, and instead directly model the C function itself using time-series techniques such as ARMA and more complex variants.
I hope this helps,
Srikant Krishna
Related
I am here to ask some small information regarding qlickview function and whether qlickview has some option regarding the prediction function or not
My Requirements:
I have some sales data from 2013 and 2014 and I want to predict the sales for 2015 what functions I can use to predict this specific data in qlickview ?
And not only sales but I have similar data for production and training for specific location and machine so if this works successfully for sales I can implement the predictions for other departments too
As there are lot of techniques and methods related to predictions I want to know which technique I need to apply in qlickview and how ?
Thank you
As you said, there are a lot of techniques and methods and you would have to combine them in QlikView as there's no one function that can do it for you. I would look into time series modelling (https://en.wikipedia.org/wiki/Time_series)
There's a good 3 part video tutorial on Youtube about time series modelling (https://www.youtube.com/watch?v=gHdYEZA50KE&feature=youtu.be). Although it is done in Excel, you can apply the same techniques in QlikView.
You would probably have to use linear regression. QlikView provides some analytical functions which you can use to calculate the slope and the y-intercept of a linear regression (linest_m and linest_b).
All in all I have found QlikView not to be very good at calculating such things. For example, if you find that instead of linear regression, polynomial regression fits your data better then you would have to implement a lot of it by yourself. Maybe it would be wise to use some statistical programming language (e.g. R, Octave) and present the results in QlikView.
Questions
I want to classify/categorize/cluster/group together a set of several thousand websites. There's data that we can train on, so we can do supervised learning, but it's not data that we've gathered and we're not adamant about using it -- so we're also considering unsupervised learning.
What features can I use in a machine learning algorithm to deal with multilingual data? Note that some of these languages might not have been dealt with in the Natural Language Processing field.
If I were to use an unsupervised learning algorithm, should I just partition the data by language and deal with each language differently? Different languages might have different relevant categories (or not, depending on your psycholinguistic theoretical tendencies), which might affect the decision to partition.
I was thinking of using decision trees, or maybe Support Vector Machines (SVMs) to allow for more features (from my understanding of them). This post suggests random forests instead of SVMs. Any thoughts?
Pragmatical approaches are welcome! (Theoretical ones, too, but those might be saved for later fun.)
Some context
We are trying to classify a corpus of many thousands of websites in 3 to 5 languages (maybe up to 10, but we're not sure).
We have training data in the form of hundreds of websites already classified. However, we may choose to use that data set or not -- if other categories make more sense, we're open to not using the training data that we have, since it is not something we gathered in the first place. We are on the final stages of scraping data/text from websites.
Now we must decide on the issues above. I have done some work with the Brown Corpus and the Brill tagger, but this will not work because of the multiple-languages issue.
We intend to use the Orange machine learning package.
According to the context you have provided, this is a supervised learning problem.
Therefore, you are doing classification, not clustering. If I misunderstood, please update your question to say so.
I would start with the simplest features, namely tokenize the unicode text of the pages, and use a dictionary to translate every new token to a number, and simply consider the existence of a token as a feature.
Next, I would use the simplest algorithm I can - I tend to go with Naive Bayes, but if you have an easy way to run SVM this is also nice.
Compare your results with some baseline - say assigning the most frequent class to all the pages.
Is the simplest approach good enough? If not, start iterating over algorithms and features.
If you go the supervised route, then the fact that the web pages are in multiple languages shouldn't make a difference. If you go with, say lexical features (bag-o'-words style) then each language will end up yielding disjoint sets of features, but that's okay. All of the standard algorithms will likely give comparable results, so just pick one and go with it. I agree with Yuval that Naive Bayes is a good place to start, and only if that doesn't meet your needs that try something like SVMs or random forests.
If you go the unsupervised route, though, the fact that the texts aren't all in the same language might be a big problem. Any reasonable clustering algorithm will first group the texts by language, and then within each language cluster by something like topic (if you're using content words as features). Whether that's a bug or a feature will depend entirely on why you want to classify these texts. If the point is to group documents by topic, irrespective of language, then it's no good. But if you're okay with having different categories for each language, then yeah, you've just got as many separate classification problems as you have languages.
If you do want a unified set of classes, then you'll need some way to link similar documents across languages. Are there any documents in more that one language? If so, you could use them as a kind of statistical Rosetta Stone, to link words in different languages. Then, using something like Latent Semantic Analysis, you could extend that to second-order relations: words in different languages that don't ever occur in the same document, but which tend to co-occur with words which do. Or maybe you could use something like anchor text or properties of the URLs to assign a rough classification to documents in a language-independent manner and use that as a way to get started.
But, honestly, it seems strange to go into a classification problem without a clear idea of what the classes are (or at least what would count as a good classification). Coming up with the classes is the hard part, and it's the part that'll determine whether the project is a success or failure. The actual algorithmic part is fairly rote.
Main answer is: try different approaches. Without actual testing it's very hard to predict what method will give best results. So, I'll just suggest some methods that I would try first and describe their pros and cons.
First of all, I would recommend supervised learning. Even if the data classification is not very accurate, it may still give better results than unsupervised clustering. One of the reasons for it is a number of random factors that are used during clustering. For example, k-means algorithm relies on randomly selected points when starting the process, which can lead to a very different results for different program runnings (though x-means modifications seems to normalize this behavior). Clustering will give good results only if underlying elements produce well separated areas in the feature space.
One of approaches to treating multilingual data is to use multilingual resources as support points. For example, you can index some Wikipedia's articles and create "bridges" between same topics in different languages. Alternatively, you can create multilingual association dictionary like this paper describes.
As for methods, the first thing that comes to mind is instance-based semantic methods like LSI. It uses vector space model to calculate distance between words and/or documents. In contrast to other methods it can efficiently treat synonymy and polysemy. Disadvantage of this method is a computational inefficiency and leak of implementations. One of the phases of LSI makes use of a very big cooccurrence matrix, which for large corpus of documents will require distributed computing and other special treatment. There's modification of LSA called Random Indexing which do not construct full coocurrence matrix, but you'll hardly find appropriate implementation for it. Some time ago I created library in Clojure for this method, but it is pre-alpha now, so I can't recommend using it. Nevertheless, if you decide to give it a try, you can find project 'Clinch' of a user 'faithlessfriend' on github (I'll not post direct link to avoid unnecessary advertisement).
Beyond special semantic methods the rule "simplicity first" must be used. From this point, Naive Bayes is a right point to start from. The only note here is that multinomial version of Naive Bayes is preferable: my experience tells that count of words really does matter.
SVM is a technique for classifying linearly separable data, and text data is almost always not linearly separable (at least several common words appear in any pair of documents). It doesn't mean, that SVM cannot be used for text classification - you still should try it, but results may be much lower than for other machine learning tasks.
I haven't enough experience with decision trees, but using it for efficient text classification seems strange to me. I have seen some examples where they gave excellent results, but when I tried to use C4.5 algorithm for this task, the results were terrible. I believe you should get some software where decision trees are implemented and test them by yourself. It is always better to know then to suggest.
There's much more to say on every topic, so feel free to ask more questions on specific topic.
I am new to machine learning. I am familiar with SVM , Neural networks and GA. I'd like to know the best technique to learn for classifying pictures and audio. SVM does a decent job but takes a lot of time. Anyone know a faster and better one? Also I'd like to know the fastest library for SVM.
Your question is a good one, and has to do with the state of the art of classification algorithms, as you say, the election of the classifier depends on your data, in the case of images, I can tell you that there is one method called Ada-Boost, read this and this to know more about it, in the other hand, you can find lots of people are doing some researh, for example in Gender Classification of Faces Using Adaboost [Rodrigo Verschae,Javier Ruiz-del-Solar and Mauricio Correa] they say:
"Adaboost-mLBP outperforms all other Adaboost-based methods, as well as baseline methods (SVM, PCA and PCA+SVM)"
Take a look at it.
If your main concern is speed, you should probably take a look at VW and generally at stochastic gradient descent based algorithms for training SVMs.
if the number of features is large in comparison to the number of the trainning examples
then you should go for logistic regression or SVM without kernel
if the number of features is small and the number of training examples is intermediate
then you should use SVN with gaussian kernel
is the number of features is small and the number of training examples is large
use logistic regression or SVM without kernels .
that's according to the stanford ML-class .
For such task you may need to extract features first. Only after that classification is feasible.
I think feature extraction and selection is important.
For image classification, there are a lot of features such as raw pixels, SIFT feature, color, texture,etc. It would be better choose some suitable for your task.
I'm not familiar with audio classication, but there may be some specturm features, like the fourier transform of the signal, MFCC.
The methods used to classify is also important. Besides the methods in the question, KNN is a reasonable choice, too.
Actually, using what feature and method is closely related to the task.
The method mostly depends on problem at hand. There is no method that is always the fastest for any problem. Having said that, you should also keep in mind that once you choose an algorithm for speed, you will start compromising on the accuracy.
For example- since your trying to classify images, there might a lot of features compared to the number of training samples at hand. In such cases, if you go for SVM with kernels, you could end up over fitting with the variance being too high.
So you would want to choose a method that has a high bias and low variance. Using logistic regression or linear SVM are some ways to do it.
You could also use different types of regularizations or techniques such as SVD to remove the features that do not contribute much to your output prediction and have only the most important ones. In other words, choose the features that have little or no correlation between them. Once you do this, you would be able to speed yup your SVM algorithms without sacrificing the accuracy.
Hope it helps.
there are some good techniques in learning machines such as, boosting and adaboost.
One method of classification is the boosting method. This method will iteratively manipulate data which will then be classified by a particular base classifier on each iteration, which in turn will build a classification model. Boosting uses weighting of each data in each iteration where its weight value will change according to the difficulty level of the data to be classified.
While the method adaBoost is one ensamble technique by using loss function exponential function to improve the accuracy of the prediction made.
I think your question is very open ended, and "best classifier for images" will largely depend on the type of image you want to classify. But in general, I suggest you study convulutional neural networks ( CNN ) and transfer learning, currently these are the state of the art techniques for the problem.
check out pre-trained models of cnn based neural networks from pytorch or tensorflow
Related to images I suggest you also study pre-processing of images, pre-processing techniques are very important to highlight some feature of the image and improve the generalization of the classifier.
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We need to decide between Support Vector Machines and Fast Artificial Neural Network for some text processing project.
It includes Contextual Spelling Correction and then tagging the text to certain phrases and their synonyms.
Which will be the right approach? Or is there an alternate to both of these... Something more appropriate than FANN as well as SVM?
I think you'll get a competitive results from both of the algorithms, so you should aggregate the results... think about ensemble learning.
Update:
I don't know if this is specific enough: use Bayes Optimal Classifier to combine the prediction from each algorithm. You have to train both of your algorithms, then you have to train the Bayes Optimal Classifier to use your algorithms and make optimal predictions based on the input of the algorithms.
Separate your training data in 3:
1st data set will be used to train the (Artificial) Neural Network and the Support Vector Machines.
2nd data set will be used to train the Bayes Optimal Classifier by taking the raw predictions from the ANN and SVM.
3rd data set will be your qualification data set where you will test your trained Bayes Optimal Classifier.
Update 2.0:
Another way to create an ensemble of the algorithms is to use 10-fold (or more generally, k-fold) cross-validation:
Break data into 10 sets of size n/10.
Train on 9 datasets and test on 1.
Repeat 10 times and take a mean accuracy.
Remember that you can generally combine many the classifiers and validation methods in order to produce better results. It's just a matter of finding what works best for your domain.
You might want to also take a look at maxent classifiers (/log linear models).
They're really popular for NLP problems. Modern implementations, which use quasi-newton methods for optimization rather than the slower iterative scaling algorithms, train more quickly than SVMs. They also seem to be less sensitive to the exact value of the regularization hyperparameter. You should probably only prefer SVMs over maxent, if you'd like to use a kernel to get feature conjunctions for free.
As for SVMs vs. neural networks, using SVMs would probably be better than using ANNs. Like maxent models, training SVMs is a convex optimization problem. This means, given a data set and a particular classifier configuration, SVMs will consistently find the same solution. When training multilayer neural networks, the system can converge to various local minima. So, you'll get better or worse solutions depending on what weights you use to initialize the model. With ANNs, you'll need to perform multiple training runs in order to evaluate how good or bad a given model configuration is.
This question is very old. Lot of developments were happened in NLP area in last 7 years.
Convolutional_neural_network and Recurrent_neural_network evolved during this time.
Word Embeddings: Words appearing within similar context possess similar meaning. Word embeddings are pre-trained on a task where the objective is to predict a word based on its context.
CNN for NLP:
Sentences are first tokenized into words, which are further transformed into a word embedding matrix (i.e., input embedding layer) of d dimension.
Convolutional filters are applied on this input embedding layer to produce a feature map.
A max-pooling operation on each filter obtain a fixed length output and reduce the dimensionality of the output.
Since CNN had a short-coming of not preserving long-distance contextual information, RNNs have been introduced.
RNNs are specialized neural-based approaches that are effective at processing sequential information.
RNN memorizes the result of previous computations and use it in current computation.
There are few variations in RNN - Long Short Term Memory Unit (LSTM) and Gated recurrent units (GRUs)
Have a look at below resources:
deep-learning-for-nlp
Recent trends in deep learning paper
You can use Convolution Neural Network (CNN) or Recurrent Neural Network (RNN) to train NLP. I think CNN has achieved state-of-the-art now.
I've got a classification problem in my hand, which I'd like to address with a machine learning algorithm ( Bayes, or Markovian probably, the question is independent on the classifier to be used). Given a number of training instances, I'm looking for a way to measure the performance of an implemented classificator, with taking data overfitting problem into account.
That is: given N[1..100] training samples, if I run the training algorithm on every one of the samples, and use this very same samples to measure fitness, it might stuck into a data overfitting problem -the classifier will know the exact answers for the training instances, without having much predictive power, rendering the fitness results useless.
An obvious solution would be seperating the hand-tagged samples into training, and test samples; and I'd like to learn about methods selecting the statistically significant samples for training.
White papers, book pointers, and PDFs much appreciated!
You could use 10-fold Cross-validation for this. I believe it's pretty standard approach for classification algorithm performance evaluation.
The basic idea is to divide your learning samples into 10 subsets. Then use one subset for test data and others for train data. Repeat this for each subset and calculate average performance at the end.
As Mr. Brownstone said 10-fold Cross-Validation is probably the best way to go. I recently had to evaluate the performance of a number of different classifiers for this I used Weka. Which has an API and a load of tools that allow you to easily test the performance of lots of different classifiers.