Eg: For training, you use data for which users have filled up all the fields (around 40 fields) in a form along with an expected output.
We now build a model (could be an artificial neural net or SVM or logistic regression, etc).
Finally, a user now enters 3 fields in the form and expects a prediction.
In this scenario, what is the best ML algorithm I can use?
I think it will depend on the specific context of your problem. What are you trying to predict based on what kind of input?
For example, recommender systems are used by companies like Netflix to predict a user's rating of, for example, movies based on a very sparse feature vector (user's existing ratings of a tiny percentage of all of the movies in the catalog).
Another option is to develop some mapping algorithm from your sparse feature space to a common latent space on which you perform your classification with, e.g., an SVM or neural network. I believe this paper does something similar. You can also look in to papers like this one for a classifier that translates data from two different domains (your training vs. testing set, for example, where both contain similar information, but one has complete data and the other does not) into a common latent space for classification. There is a lot out there actually on domain-independent classification.
Keywords to look up (with some links to get you started): generative adversarial networks (GAN), domain-adversarial training, domain-independent classification, transfer learning.
Related
I have got a new task(not traditional) from my client, It is something about machine learning.
As I have never been to "machine learning" except some little Data Mining stuff so I need your help.
My task is to Classify a product present on any Shopping Site, on the basis of gender(whom the product belongs to),agegroup etc, the training data we can have is the product's Title, Keywords(available in the html of the product page), and product description.
I did a lot of R&D , I found Image Recog APIs(cloudsight,vufind) that returned the details of the product image but that did not full fill the need, used google suggestqueries, searched out many machine learning algorithms and finally...
I came to know about the "Decision Tree Learning Algorithm" but cannot figure out, how it is applicable to my problem.
I tried out the "PlayingTennis" dataset but couldn't make the sense what to do.
Can you give me some direction that from where to start this journey? Should I focus on The Decision Tree Learning algorithm or Is there any other algorithm you would suggest I should focus on to categorize the products on the basis of context?
If you say , I would share in detail about what things I searched about to solve my problem.
I would suggest to do the following:
Go through items in your dataset and classify them manually (decide for which gender each item is). Store each decision so that you would be able to somehow link each item in an original dataset with a target class.
Develop an algorithm for converting each item from your dataset into a feature vector. This algorithm should be able to convert each item in your original dataset in a vector of numbers (more about how to do it later).
Convert all your dataset with appropriate classes into a dataset that would look like this:
Feature_1, Feature_2, Feature_3, ..., Gender
value_1, value_2, value_3, ... male
It would be a good decision to store it in CSV file since you would be able to load it and process in different machine learning tools (More about those later).
Load dataset you've created at step 3 in machine learning tool of your choice and try to come up with the best model that can classify items in your dataset by gender.
Store model created at step 4. It will be part of your production system.
Develop a production code that can convert an unclassified product, create feature vector out of it and pass this feature vector to the model you've saved at step 5. The result of this operation should be a predicted gender.
Details
If there too many items (say tens of thousands) in your original dataset it may be impractical to classify them yourself. What you can do is to use Amazon Mechanical Turk to simplify your task. If you are unable to use it (the last time I've checked you had to have a USA address to use it) you can just classify few hundreds of items to start working on your model and classify the rest to improve accuracy of your classification (the more training data you use the better the accuracy, but up to a certain point)
How to extract features from a dataset
If keyword has form like tag=true/false, it's a boolean feature.
If keyword has form like tag=42, it's a numerical one or ordinal. For example it can be price value or price range (0-10, 10-50, 50-100, etc.)
If keyword has form like tag=string_value you can convert it into a categorical value
A class (gender) is simply boolean value 0/1
You can experiment a bit with how you extract your features, since it may influence the result accuracy.
How to extract features from product description
There are different ways to convert a text into a feature vector. Look for TF-IDF algorithms or something similar.
Machine learning tools
You can use one of existing machine learning libraries and hack some code that loads your CSV dataset, trains a model and checks the accuracy, but at first I would suggest to use something like Weka. It has more or less intuitive UI and you can quickly start to experiment with different machine learning algorithms, convert different features in your dataset from string to categories, or from real values to ordinal values, etc. Good thing about Weka is that it has Java API, so you can automate all the process of data conversion, train models programmatically, etc.
What algorithms to choose
I would suggest to use decision tree algorithms like C4.5. It's fast and show good results on wide range of machine learning tasks. Additionally you can use ensemble of classifiers. There are various algorithms that can combine several algorithms like (google for boosting or random forest to find out more) usually they give better results, but work more slowly (since you need to run a single feature vector through several algorithms.
One another trick that you can use to make your algorithm more accurate is to use models that work on different sets of features (say one algorithm uses features extracted from tags and another algorithm uses data extracted from product description). You can then combine them using algorithms like stacking to come up with a final result.
For classification on the basis of features extracted from text, you can try to use Naive Bayes algorithm or SVM. They both show good results in text classification.
Do consider Support Vector Classifier (SVC), or for Google's sake the Support Vector Machine (SVM). If You have a large training set (which I suspect) search for implementations that are "fast" or "scalable".
I am tasked with detecting anomalies (known or unknown) using machine-learning algorithms from data in various formats - e.g. emails, IMs etc.
What are your favorite and most effective anomaly detection algorithms?
What are their limitations and sweet-spots?
How would you recommend those limitations be addressed?
All suggestions very much appreciated.
Statistical filters like Bayesian filters or some bastardised version employed by some spam filters are easy to implement. Plus there are lots of online documentation about it.
The big downside is that it cannot really detect unknown things. You train it with a large sample of known data so that it can categorize new incoming data. But you can turn the traditional spam filter upside down: train it to recognize legitimate data instead of illegitimate data so that anything it doesn't recognize is an anomaly.
There are various types of anomaly detection algorithms, depending on the type of data and the problem you are trying to solve:
Anomalies in time series signals:
Time series signals is anything you can draw as a line graph over time (e.g., CPU utilization, temperature, rate per minute of number of emails, rate of visitors on a webpage, etc). Example algorithms are Holt-Winters, ARIMA models, Markov Models, and more. I gave a talk on this subject a few months ago - it might give you more ideas about algorithms and their limitations.
The video is at: https://www.youtube.com/watch?v=SrOM2z6h_RQ
Anomalies in Tabular data: These are cases where you have feature vector that describe something (e.g, transforming an email to a feature vector that describes it: number of recipients, number of words, number of capitalized words, counts of keywords, etc....). Given a large set of such feature vectors, you want to detect some that are anomalies compared to the rest (sometimes called "outlier detection"). Almost any clustering algorithm is suitable in these cases, but which one would be most suitable depends on the type of features and their behavior -- real valued features, ordinal, nominal or anything other. The type of features determine if certain distance functions are suitable (the basic requirement for most clustering algorithms), and some algorithms are better with certain types of features than others.
The simplest algo to try is k-means clustering, where an anomaly sample would be either very small clusters or vectors that are far from all cluster centers. One sided SVM can also detect outliers, and has the flexibility of choosing different kernels (and effectively different distance functions). Another popular algo is DBSCAN.
When anomalies are known, the problem becomes a supervised learning problem, so you can use classification algorithms and train them on the known anomalies examples. However, as mentioned - it would only detect those known anomalies and if the number of training samples for anomalies is very small, the trained classifiers may not be accurate. Also, because the number of anomalies is typically very small compared to "no-anomalies", when training the classifiers you might want to use techniques like boosting/bagging, with over sampling of the anomalies class(es), but optimize on very small False Positive rate. There are various techniques to do it in the literature --- one idea that I found to work many times very well is what Viola-Jones used for face detection - a cascade of classifiers. see: http://www.vision.caltech.edu/html-files/EE148-2005-Spring/pprs/viola04ijcv.pdf
(DISCLAIMER: I am the chief data scientist for Anodot, a commercial company doing real time anomaly detection for time series data).
I have been playing around with different data clustering algorithms working on finding clusters between random data points represented an nodes, I keep reading that data clustering is used for image recognition. I am failing to make the connection, how does clustering data help in recognizing an image or in facial recognition. can someone explain this?
It's no surprise that clustering is used for pattern recognition at large, and image recognition in particular: clustering is a reducing process, and images in this megapixel era need boiling down... It is also a process which produces categories and that is of course useful.
However there are many approaches to the use of clustering as a technique for image recognition. One of the reasons for this diversity is that clustering can be applied at different level, for different purposes: from basic pixel level to feature level (feature be a line, a geometric figure...), for classification or for other purposes.
At a very high level, clustering is a statistical tool, it helps discovering the relative importance of various dimensions in defining the belonging of particular item to a particular category.
One [of many] usage[s] of such a tool, is with supervised learning, whereby a set of human-selected items (say images) are fed into the cluster-based logic, along with a label associated with a particular item ("this is an apple", "this is another apple", "this is a lemon"...), the clustering logic then determines how much each dimension of the input matters for helping each group of items (apples, lemons...) fit in a distinct cluster (for example the color may matter relatively little, but the shape, or the presence of dots, or whatever may matter a lot). After this training phase, new images can be fed to the logic and by seeing how close to a particular cluster this image falls, it is "recognized" (as a banana!).
When it comes to image processing one needs to remember that whatever is "fed" to the clustering logic is not necessarily (in fact, rarely) the raw pixels, but various "objects"
characterizing various "elements" of the original data (essentially a collection of relatively high dimension vectors, not unlike some that one may have encountered in other other data clustering examples), and produced by previous stages of the process. For example a important element of facial recognition is probably the exact distance between the center of the eyes. In previous stages, the image is processed in a way that figures out where the eyes are (possibly relying on another clustering-based logic). Then the distance between the eyes, along with many other elements are fed to the final clustering logic.
The preceding description is only one example of the use of clustering for image recognition. Indeed, various forms of neural networks have been used, very successfully, in this domain, and it can be argued that in a sense these neural networks are clustering information. One of the reasons for the success of neural nets may lie in their ability to be more respectful of the locality dimension as found in the original input, and also their ability to work in a hierarchical fashion.
A good conclusion to this write up would be a short list of online resources, but I'm pressed for time at the moment... "to be continued" ;-)
Next day edit: (failed attempt to provide an introductory online bibliography on the subject)
My search for literature on the topic of clustering as applied to artificial vision and image processing revealed two distinct... clusters ;-)
Books such as Algorithms for image processing and computer vision J Parkey pub Wiley, or Machine Vision : Theory, Algorithms, Practicalities M Seul et. Al Cambridge UP. Such books generally cover the all important techniques associated with noise reduction, Edge detection, Color or intensity conversion, and many other elements of the image processing chain, most of which do not involve clustering or even statistical methods, and they reserve only a chapter or two, or even minor mentions, to clustering, as applied to pattern recognition or to other tasks.
Scholarly papers and conference handbooks, which specifically cover clustering techniques applied to artificial vision and such, but in the narrowest and deepest fashion (ex: Variations on the Fukunaga and Narendra algorithm, for applications in character recognition, or Fast methods for selections of Nearest Neighbor candidates in whatever context.)
In short I feel ill equipped to make any specific book or article suggestion.
You may find it informative to browse titles in say Google books, keying in by "Artificial vision" or "Image Recognition" or some or the titles mentioned above. With the preview feature and also the tag cloud (btw another application of clustering) found in the "about this book" link, one can get a good idea of the various books contents and maybe decide to purchase some of them. Unfortunately the reduced readership and the potentially lucrative applications in the field make these books relatively expensive. At the other end of the spectrum, you may download, sometimes for free, research papers discussing advanced topics in the field. These will also show up on regular (web) Google, or at specialized repositories such as CiteSeer.
Good luck with your exploration in that field!
If I have a large set of data that describes physical 'things', how could I go about measuring how well that data fits the 'things' that it is supposed to represent?
An example would be if I have a crate holding 12 widgets, and I know each widget weighs 1 lb, there should be some data quality 'check' making sure the case weighs 13 lbs maybe.
Another example would be that if I have a lamp and an image representing that lamp, it should look like a lamp. Perhaps the image dimensions should have the same ratio of the lamp dimensions.
With the exception of images, my data is 99% text (which includes height, width, color...).
I've studied AI in school, but have done very little outside of that.
Are standard AI techniques the way to go? If so, how do I map a problem to an algorithm?
Are some languages easier at this than others? Do they have better libraries?
thanks.
Your question is somewhat open-ended, but it sounds like you want is what is known as a "classifier" in the field of machine learning.
In general, a classifier takes a piece of input and "classifies" it, ie: determines a category for the object. Many classifiers provide a probability with this determination, and some may even return multiple categories with probabilities on each.
Some examples of classifiers are bayes nets, neural nets, decision lists, and decision trees. Bayes nets are often used for spam classification. Emails are classified as either "spam" or "not spam" with a probability.
For you question you'd want to classify your objects as "high quality" or "not high quality".
The first thing you'll need is a bunch of training data. That is, a set of objects where you already know the correct classification. One way to obtain this could be to get a bunch of objects and classify them by hand. If there are too many objects for one person to classify you could feed them to Mechanical Turk.
Once you have your training data you'd then build your classifier. You'll need to figure out what attributes are important to your classification. You'll probably need to do some experimentation to see what works well. You then have your classifier learn from your training data.
One approach that's often used for testing is to split your training data into two sets. Train your classifier using one of the subsets, and then see how well it classifies the other (usually smaller) subset.
AI is one path, natural intelligence is another.
Your challenge is a perfect match to Amazon's Mechanical Turk. Divvy your data space up into extremely small verifiable atoms and assign them as HITs on Mechanical Turk. Have some overlap to give yourself a sense of HIT answer consistency.
There was a shop with a boatload of component CAD drawings that needed to be grouped by similarity. They broke it up and set it loose on Mechanical Turk to very satisfying results. I could google for hours and not find that link again.
See here for a related forum post.
This is a tough answer. For example, what defines a lamp? I could google images a picture of some crazy looking lamps. Or even, look up the definition of a lamp (http://dictionary.reference.com/dic?q=lamp). Theres no physical requirements of what a lamp must look like. Thats the crux of the AI problem.
As for data, you could setup Unit testing on the project to ensure that 12 widget() weighs less than 13 lbs in the widetBox(). Regardless, you need to have the data at hand to be able to test things like that.
I hope i was able to answer your question somewhat. Its a bit vauge, and my answers are broad, but hopefully it'll at least send you in a good direction.
Is Latent Semantic Indexing (LSI) a Statistical Classification algorithm? Why or why not?
Basically, I'm trying to figure out why the Wikipedia page for Statistical Classification does not mention LSI. I'm just getting into this stuff and I'm trying to see how all the different approaches for classifying something relate to one another.
No, they're not quite the same. Statistical classification is intended to separate items into categories as cleanly as possible -- to make a clean decision about whether item X is more like the items in group A or group B, for example.
LSI is intended to show the degree to which items are similar or different and, primarily, find items that show a degree of similarity to an specified item. While this is similar, it's not quite the same.
LSI/LSA is eventually a technique for dimensionality reduction, and usually is coupled with a nearest neighbor algorithm to make it a into classification system. Hence in itself, its only a way of "indexing" the data in lower dimension using SVD.
Have you read about LSI on Wikipedia ? It says it uses matrix factorization (SVD), which in turn is sometimes used in classification.
The primary distinction in machine learning is between "supervised" and "unsupervised" modeling.
Usually the words "statistical classification" refer to supervised models, but not always.
With supervised methods the training set contains a "ground-truth" label that you build a model to predict. When you evaluate the model, the goal is to predict the best guess at (or probability distribution of) the true label, which you will not have at time of evaluation. Often there's a performance metric and it's quite clear what the right vs wrong answer is.
Unsupervised classification methods attempt to cluster a large number of data points which may appear to vary in complicated ways into a smaller number of "similar" categories. Data in each category ought to be similar in some kind of 'interesting' or 'deep' way. Since there is no "ground truth" you can't evaluate 'right or wrong', but 'more' vs 'less' interesting or useful.
Similarly evaluation time you can place new examples into potentially one of the clusters (crisp classification) or give some kind of weighting quantifying how similar or different looks like the "archetype" of the cluster.
So in some ways supervised and unsupervised models can yield something which is a "prediction", prediction of class/cluster label, but they are intrinsically different.
Often the goal of an unsupervised model is to provide more intelligent and powerfully compact inputs for a subsequent supervised model.