I'm reading about fuzzy logic and I just don't see how it would possibly improve machine learning algorithms in most instances (which it seems to be applied to relatively often).
Take for example, k nearest neighbors. If you have a bunch a bunch of attributes like color: [red,blue,green,orange], temperature: [real number], shape: [round, square, triangle], you can't really fuzzify any of these except for the real numbered attribute (please correct me if I'm wrong), and I don't see how this can improve anything more than bucketing things together.
How can machine fuzzy logic be used to improve machine learning? The toy examples you'll find on most websites don't seem to be all that applicable, most of the time.
Fuzzy logic is advisable when the variables have a natural shape interpretation. For example, [very few, few, many, very many] have a nice overlapping trapezoid interpretation of values.
Variables like color might not. Fuzzy variables denote degree of membership, that's when they become useful.
Regarding machine learning, it depends on what stage of the algorithm you want to apply fuzzy logic. It would be better applied in my opinion after the clusters are found (using traditional learning techniques) to determining the degree of membership of a certain point in the search space on each cluster, but that doesn't improve learning per see, but classification after learning.
[round, square, triangle] are mostly ideal categories, which exist primarily in geometry (i.e. in theory). In real world, some shapes might be almost square or more or less round (circular shape). There are many nuances of red, and some colors are closer to some others (ask a woman to explain turquoise, for example). Hence, also abstract categories and some specific values are useful as references, in real world the objects or values are not necessarily equals to these ones.
Fuzzy membership allow you to measure how far are some specific objects from some ideal. Using this measure lets one to avoid "no, it's not circular" (which might lead to information loss) and make use of the measure the given object is (not) circular.
In my view, fuzzy logic is not a practically viable approach to anything unless you are building a purpose build fuzzified controller or some rule based structure like for compliance/policies. Although, fuzzy implies dealing with everything between and including 0 and 1. It, however, I find is a bit flawed when you approach more complicated problems where you need to apply fuzzy logic aspects in 3 dimensional spaces. You can still approach multivariate without having to look at fuzzy logic. Unfortunately, for me having studied fuzzy logic I found myself disagreeing with the principles approached in fuzzy sets in large dimensional spaces it seems infeasible, unpractical, and not very logically sound. The natural language base that you would be applying in your fuzzy set solution will also be very adhoc what exactly is [very,few, many] this is all what you define in your application.
Alot, of machine learning aspects you will find that you don't even have to go so far as to build natural language underpinnings into your model. In fact, you will find you can achieve even better results without having to apply fuzzy logic into any aspect of your model.
just too irritate you a bit by forcibly adding fuzziness to this. if instead of the "shape" attribute you had a "number of sides" attribute which would have been further divided into "less", "medium", "many" and "uncountable". the square could have been a part of "less" and "medium" both given the appropriate membership function. in place of the "color" attribute, if you had "red" attribute, then using the RGB code, a membership function could have been made. so as my experience in data mining says, every method can be applied to every dataset, what works, works.
Couldn't one just convert discrete sets into continuous ones and get the same effects as fuzziness, while being able to use all the techniques of probability theory?
For instance size ['small', 'medium', 'big'] ==> [0,1]
It's not clear to me what you're trying to accomplish in the example you give (shapes, colors, etc.). Fuzzy logic has been used successfully with machine learning, but personally I think it is probably more often useful in constructing policies. Rather than go on about it, I refer you to an article I published in the Mar/Apr-2002 issue of "PC AI" magazine, which hopefully makes the idea clear:
Putting Fuzzy Logic to Work: An Introduction to Fuzzy Rules
Related
How are keyword clouds constructed?
I know there are a lot of nlp methods, but I'm not sure how they solve the following problem:
You can have several items that each have a list of keywords relating to them.
(In my own program, these items are articles where I can use nlp methods to detect proper nouns, people, places, and (?) possibly subjects. This will be a very large list given a sufficiently sized article, but I will assume that I can winnow the list down using some method by comparing articles. How to do this properly is what I am confused about).
Each item can have a list of keywords, but how do they pick keywords such that the keywords aren't overly specific or overly general between each item?
For example, trivially "the" can be a keyword that is a lot of items.
While "supercalifragilistic" could only be in one.
I suppose that I could create a heuristic where if a word exists in n% of the items where n is sufficiently small, but will return a nice sublist (say 5% of 1000 articles is 50, which seems reasonable) then I could just use that. However, the issue that I take with this approach is that given two different sets of entirely different items, there is most likely some difference in interrelatedness between the items, and I'm throwing away that information.
This is very unsatisfying.
I feel that given the popularity of keyword clouds there must have been a solution created already. I don't want to use a library however as I want to understand and manipulate the assumptions in the math.
If anyone has any ideas please let me know.
Thanks!
EDIT:
freenode/programming/guardianx has suggested https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf is ok btw, but the issue is that the weighting needs to be determined apriori. Given that two distinct collections of documents will have a different inherent similarity between documents, assuming an apriori weighting does not feel correct
freenode/programming/anon suggested https://en.wikipedia.org/wiki/Word2vec
I'm not sure I want something that uses a neural net (a little complicated for this problem?), but still considering.
Tf-idf is still a pretty standard method for extracting keywords. You can try a demo of a tf-idf-based keyword extractor (which has the idf vector, as you say apriori determined, estimated from Wikipedia). A popular alternative is the TextRank algorithm based on PageRank that has an off-the-shelf implementation in Gensim.
If you decide for your own implementation, note that all algorithms typically need plenty of tuning and text preprocessing to work correctly.
The minimum you need to do is removing stopwords that you know that they never can be a keyword (prepositions, articles, pronouns, etc.). If you want something fancier, you can use for instance Spacy to keep only desired parts of speech (nouns, verbs, adjectives). You can also include frequent multiword expressions (gensim has good function for automatic collocation detection), named entities (spacy can do it). You can get better results if you run coreference resolution and substitute pronouns with what they refer to... There are endless options for improvements.
I'm looking for a supervised machine learning algorithm that would produce transparent rules or definitions that can be easily interpreted by a human.
Most algorithms that I work with (SVMs, random forests, PLS-DA) are not very transparent. That is, you can hardly summarize the models in a table in a publication aimed at a non-computer scientist audience. What authors usually do is, for example, publish a list of variables that are important based on some criterion (for example, Gini index or mean decrease of accuracy in the case of RF), and sometimes improve this list by indicating how these variables differ between the classes in question.
What I am looking is a relatively simple output of the style "if (any of the variables V1-V10 > median or any of the variables V11-V20 < 1st quartile) and variable V21-V30 > 3rd quartile, then class A".
Is there any such thing around?
Just to constraint my question a bit: I am working with highly multidimensional data sets (tens of thousands to hundreds of thousands of often colinear variables). So for example regression trees would not be a good idea (I think).
You sound like you are describing decision trees. Why would regression trees not be a good choice? Maybe not optimal, but they work, and those are the most directly interpretable models. Anything that works on continuous values works on ordinal values.
There's a tension between wanting an accurate classifier, and wanting a simple and explainable model. You could build a random decision forest model, and constrain it in several ways to make it more interpretable:
Small max depth
High minimum information gain
Prune the tree
Only train on "understandable" features
Quantize/round decision threhsolds
The model won't be as good, necessarily.
You can find interesting research in the understanding AI methods done by Been Kim at Google Brain.
I'm not sure if this is the best place to ask this, but you guys have been helpful with plenty of my CS homework in the past so I figure I'll give it a shot.
I'm looking for an algorithm to blindly combine several dependent variables into an index that produces the best linear fit with an external variable. Basically, it would combine the dependent variables using different mathematical operators, include or not include each one, etc. until an index is developed that best correlates with my external variable.
Has anyone seen/heard of something like this before? Even if you could point me in the right direction or to the right place to ask, I would appreciate it. Thanks.
Sounds like you're trying to do Multivariate Linear Regression or Multiple Regression. The simplest method (Read: less accurate) to do this is to individually compute the linear regression lines of each of the component variables and then do a weighted average of each of the lines. Beyond that I am afraid I will be of little help.
This appears to be simple linear regression using multiple explanatory variables. As the implication here is that you are using a computational approach you could do something as simple apply a linear model to your data using every possible combination of your explanatory variables that you have (whether you want to include interaction effects is your choice), choose a goodness of fit measure (R^2 being just one example) and use that to rank the fit of each model you fit?? The quality of a model is also somewhat subjective in many fields - you could reject a model containing 15 variables if it only moderately improves the fit over a far simpler model just containing 3 variables. If you have not read it already I don't doubt that you will find many useful suggestions in the following text :
Draper, N.R. and Smith, H. (1998).Applied Regression Analysis Wiley Series in Probability and Statistics
You might also try doing a google for the LASSO method of model selection.
The thing you're asking for is essentially the entirety of regression analysis.
this is what linear regression does, and this is a good portion of what "machine learning" does (machine learning is basically just a name for more complicated regression and classification algorithms). There are hundreds or thousands of different approaches with various tradeoffs, but the basic ones frequently work quite well.
If you want to learn more, the coursera course on machine learning is a great place to get a deeper understanding of this.
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.