Do you have an example or an explanation of ANFIS (Adaptive Neuro-Fuzzy Inference System), I am reading that this could be applied to classify some diseases, What do you think about it?
Usually in order to develop a fuzzy system you have to determine the if-then rules, suitable membership functions, and their parameters. This is not always a trivial task, especially the development of correct if-then rules may be time consuming as we first have to "extract" the expert knowledge somehow.
This is where ANFIS comes into play: Under certain circumstances it can automatically determine suitable parameters for the membership functions. This is the case in particular when we already have a set of input and related output variables and values. Like in an artificial neural network the ANFIS system is able to adapt its nodes and connections between them "automatically".
To your question: you could of course create an ANFIS system for your desease classification, as long as you already have input and output data for system training available. But its not necessarily tied to such systems, you can see ANFIS more an approach usable under the mentioned circumstances, than a tool for a specific problem. It all depends on the requirements for the system you want to create, as well as the known (external) preconditions...
Hope that helps!
As Matthias said ANFIS is not mapped to a particular problem, you can use it on the basis of problem requirement. But where to use ANFIS: You can use it with any problem where something is ambiguous.
Actually this is the property of FIS(Fuzzy Inference System). Adaptive come in role as Matthias explained.
For ex. took famous classification problem, classifying a input to any class is not always perfectly determined, it somewhat ambiguous. So there using ANFIS may give better results then other classification algorithms depending upon whether you are able to model the system correctly or not using ANFIS.
But using ANFIS is computationally expensive as compared to other non-fuzzy approches. As to make FIS to perfect model your problem you will add AN part to it. This only make membership function selection adaptive. What about if-then rules. For that you have to do unsupervised rule selection from the complete possible rule base(this is basically a kind of unsupervised clustering problem, where you are trying to group all the rules whose effect would be same).
So far I have found a university 'Monash' that explains (based on the guide of Matlabs's Fuzzy Logic Toolbox) ANFIS.
The fuzzy inference system that we have considered is a model that maps:
input characteristics to input
membership functions
input
membership function to rules
rules
to a set of output characteristics
output characteristics to output
membership functions
the output membership function to a
single-valued output, or, a decision
associated with the output.
Yes it can be used for Diseases Classification.
Since the idea of ANFIS is combine fuzzy system in architecture of ANN. In this case, ANFIS have two main benefit.
first, you can use fuzzy variable which is support for Linguistic variable and it's fit for Diseases's symptoms that are commonly used as system's input (example of input >> pain levels : low, mid, high).
Second, since the architecture is mapped to ANN layers, ANFIS can do training process which aims to create more accurate result (ex : use Backpropagation method).
Related
I'm working on a flight data analysis project. the flight data is represented in a tabular format. Each quarter of a second, we have the status of different parameters including turobreactor parameters and avionic parameters. I intend to use an expert system to analyse the flight data in order to detect anomalies during the flight. for example T4 (temperature) shouldn't surpass 750 °C over 30 seconds. Is the expert system architecture appropriate to such task?
Every expert system consists of the knowledge base and the inference engine.
If you are going to to use the expert system architecture:
you have to make sure that you have this knowledge gathered from factual and heuristic knowledge. Those are the rules, mostly consisting of an IF part and a THEN part.
how you will apply this rules, is defined by the inference engine - the problem-solving model, where the common paradigm is chaining of IF-THEN rules (e.g. forward chaining and backward chaining).
Now answering your question, to me your example looks like a specification of a discrete cyber-physical system (Depending on other specifications can be considered hybrid too). A cyber-physical system can also be considered as a state machine which is a system that exists in a limited number of conditions and has forbidden states and progresses from one state to the next according to a fixed set of rules. In addition, if you had possible input and output events in your example, you could design Moore, Mealy machines, Petri Nets, Statecharts of your state machine, given the specifications and then use formal verification techniques to verify it.
My professor asked my class to make a neural network to try to predict if a breast cancer is benign or malignant. To do this I'm using the Breast Cancer Wisconsin (Diagnostic) Data Set.
As a tip for doing this my professor said not all 30 atributes needs to be used as an input (there are 32, but the first 2 are the ID and Diagnosis), what I want to ask is: How am I supposed to take those 30 inputs (that would create like 100+ weights depending on how many neurons I would use) and get them into a lesser number?
I've already found how to "prune" a neural net, but I don't think that's what I want. I'm not trying to eliminate unnecessary neurons, but to shrink the input itself.
PS: Sorry for any english errors, it's not my native language.
That is a question that is being under research right now. It is called feature selection and there are some techniques already. One is Principal Componetns Analysis (PCA) that reduces the dimensionality of your dataset taking those feature that keeps the most variance. Another thing you can do is to see if there are highly corelated variables. If two inputs are highly correlated may mean that they carry almost the same information so it may be remove without worsen much the performance of your classifier. As a third technique you could use is deep-learning which is a technique that tries to learn the features that will later be used to feed your trainer. More info about deep learning and PCA can be found here http://deeplearning.stanford.edu/wiki/index.php/Main_Page
This problem is called feature selection. It is mostly the same for neural networks as for other classifiers. You could prune your dataset while retaining the most variance using PCA. To go further, you could use a greedy approach and evaluate your features one by one by training and testing your network with each feature excluded in turn.
There is a technique for feature selection using just neural networks
Split your dataset into three groups:
Training data used for supervised training
Validation data used to verify that the neural network is able to generalize
Accuracy testing used to test which of the features are required
The steps:
Train a network on your training and validation set, just like you would normally do.
Test the accuracy of the network with the third dataset.
Locate the varible which yields the smallest drop in the accuracy test above when dropped (dropped meaning always feeding a zero as the input signal )
Retrain your network with the new selection of features
Keep doing this either to the network fails to be trained or there is just one variable left.
Here is a paper on the technique
I have experience dealing with Neural Networks, specifically ones of the Back-Propagating nature, and I know that of the inputs passed to the trainer, dependencies between inputs are part of the resulting models knowledge when a hidden layer is introduced.
Is the same true for decision networks?
I have found that information around these algorithms (ID3) etc somewhat hard to find. I have been able to find the actual algorithms, but information such as expected/optimal dataset formats and other overviews are rare.
Thanks.
Decision Trees are actually very easy to provide data to because all they need is a table of data, and which column out of that data what feature (or column) you want to predict on. That data can be discrete or continuous for any feature. Now there are several flavors of decision trees with different support for continuous and discrete values. And they work differently so understanding how each one works can be challenging.
Different decision tree algorithms with comparison of complexity or performance
Depending on the type of algorithm you are interested in it can be hard to find information without reading the actual papers if you want to try and implement it. I've implemented the CART algorithm, and the only option for that was to find the original 200 page book about it. Most of other treatments only discuss ideas like splitting with enough detail, but fail to discuss any other aspect at more than a high level.
As for if they take into account the dependencies between things. I believe it only assumes dependence between each input feature and the prediction feature. If the input was independent from the prediction feature you couldn't use it as a split criteria. But, between other input features I believe they must be independent of each other. I'd have to check the book to ensure that was true or not, but off the top of my head I think that's true.
Specifically, their most recent implementation.
http://www.numenta.com/htm-overview/htm-algorithms.php
Essentially, I'm asking whether non-euclidean relationships, or relationships in patterns that exceed the dimensionality of the inputs, can be effectively inferred by the algorithm in its present state?
HTM uses Euclidean geometry to determine "neighborship" when analyzing patterns. Consistently framed input causes the algorithm to exhibit predictive behavior, and sequence length is practically unlimited. This algorithm learns very well - but I'm wondering whether it has the capacity to infer nonlinear attributes from its input data.
For example, if you input the entire set of texts from Project Gutenberg, it's going to pick up on the set of probabilistic rules that comprise English spelling, grammar, and readily apparent features from the subject matter, such as gender associations with words, and so forth. These are first level "linear" relations, and can be easily defined with probabilities in a logical network.
A nonlinear relation would be an association of assumptions and implications, such as "Time flies like an arrow, fruit flies like a banana." If correctly framed, the ambiguity of the sentence causes a predictive interpretation of the sentence to generate many possible meanings.
If the algorithm is capable of "understanding" nonlinear relations, then it would be able to process the first phrase and correctly identify that "Time flies" is talking about time doing something, and "fruit flies" are a type of bug.
The answer to the question is probably a simple one to find, but I can't decide either way. Does mapping down the input into a uniform, 2d, Euclidean plane preclude the association of nonlinear attributes of the data?
If it doesn't prevent nonlinear associations, my assumption would then be that you could simply vary the resolution, repetition, and other input attributes to automate the discovery of nonlinear relations - in effect, adding a "think harder" process to the algorithm.
From what I understand of HTM's, the structure of layers and columns mimics the structure of the neocortex. See appendix B here: http://www.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf
So the short answer would be that since the brain can understand non-linear phenomenon with this structure, so can an HTM.
Initial, instantaneous sensory input is indeed mapped to 2D regions within an HTM. This does not limit HTM's to dealing with 2D representations any more than a one dimensional string of bits is limited to representing only one dimensional things. It's just a way of encoding stuff so that sparse distributed representations can be formed and their efficiencies can be taken advantage of.
To answer your question about Project Gutenberg, I don't think an HTM will really understand language without first understanding the physical world on which language is based and creates symbols for. That said, this is a very interesting sequence for an HTM, since predictions are only made in one direction, and in a way the understanding of what's happening to the fruit goes backwards. i.e. I see the pattern 'flies like a' and assume the phrase applies to the fruit the same way it did to time. HTM's do group subsequent input (words in this case) together at higher levels, so if you used Fuzzy Grouping (perhaps) as Davide Maltoni has shown to be effective, the two halves of the sentence could be grouped together into the same high level representation and feedback could be sent down linking the two specific sentences. Numenta, to my knowledge has not done too much with feedback messages yet, but it's definitely part of the theory.
The software which runs the HTM is called NuPIC (Numenta Platform for Intelligent Computing). A NuPIC region (representing a region of neocortex) can be configured to either use topology or not, depending on the type of data it's receiving.
If you use topology, the usual setup maps each column to a set of inputs which is centred on the corresponding position in the input space (the connections will be selected randomly according to a probability distribution which favours the centre). The spatial pattern recognising component of NuPIC, known as the Spatial Pooler (SP), will then learn to recognise and represent localised topological features in the data.
There is absolutely no restriction on the "linearity" of the input data which NuPIC can learn. NuPIC can learn sequences of spatial patterns in extremely high-dimensional spaces, and is limited only by the presence (or lack of) spatial and temporal structure in the data.
To answer the specific part of your question, yes, NuPIC can learn non-Euclidean and non-linear relationships, because NuPIC is not, and cannot be modelled by, a linear system. On the other hand, it seems logically impossible to infer relationships of a dimensionality which exceeds that of the data.
The best place to find out about HTM and NuPIC, its Open Source implementation, is at NuPIC's community website (and mailing list).
Yes, It can do non-linear. Basically it is multilayer. And all multilayer neural networks can infer non linear relationships. And I think the neighborship is calculated locally. If it is calcualted locally then globally it can be piece wise non linear for example look at Local Linear Embedding.
Yes HTM uses euclidean geometry to connect synapses, but this is only because it is mimicking a biological system that sends out dendrites and creates connections to other nearby cells that have strong activation at that point in time.
The Cortical Learning Algorithm (CLA) is very good at predicting sequences, so it would be good at determining "Time flies like an arrow, fruit flies like a" and predict "banana" if it has encountered this sequence before or something close to it. I don't think it could infer that a fruit fly is a type of insect unless you trained it on that sequence. Thus the T for Temporal. HTMs are sequence association compressors and retrievers (a form of memory). To get the pattern out of the HTM you play in a sequence and it will match the strongest representation it has encountered to date and predict the next bits of the sequence. It seems to be very good at this and the main application for HTMs right now are predicting sequences and anomalies out of streams of data.
To get more complex representations and more abstraction you would cascade a trained HTMs outputs to another HTMs inputs along with some other new sequence based input to correlate to. I suppose you could wire in some feedback and do some other tricks to combine multiple HTMs, but you would need lots of training on primitives first, just like a baby does, before you will ever get something as sophisticated as associating concepts based on syntax of the written word.
ok guys, dont get silly, htms just copy data into them, if you want a concept, its going to be a group of the data, and then you can have motor depend on the relation, and then it all works.
our cortex, is probably way better, and actually generates new images, but a computer cortex WONT, but as it happens, it doesnt matter, and its very very useful already.
but drawing concepts from a data pool, is tricky, the easiest way to do it is by recording an invarient combination of its senses, and when it comes up, associate everything else to it, this will give you organism or animal like intelligence.
drawing harder relations, is what humans do, and its ad hoc logic, imagine a set explaining the most ad hoc relation, and then it slowly gets more and more specific, until it gets to exact motor programs... and all knowledge you have is controlling your motor, and making relations that trigger pathways in the cortex, and tell it where to go, from the blast search that checks all motor, and finds the most successful trigger.
woah that was a mouthful, but watch out dummies, you wont get no concepts from a predictive assimilator, which is what htm is, unless you work out how people draw relations in the data pool, like a machine, and if you do that, its like a program thats programming itself.
no shit.
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.