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.
Related
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.
I'm starting up looking into doing some machine translation of search queries, and have been trying to think of different ways to rate my translation system between iterations and against other systems. The first thing that comes to mind is getting translations of a set of search terms from mturk from a bunch of people and saying each is valid, or something along those lines, but that would be expensive, and possibly prone to people putting in bad translations.
Now that I'm trying to think of something cheaper or better, I figured I'd ask StackOverflow for ideas, in case there's already some standard available, or someone has tried to find one of these before. Does anyone know, for example, how Google Translate rates various iterations of their system?
There is some information here that might be useful as it provides a basic explanation of the BLEU scoring technique that is often used to measure the quality of an MT system by developers.
The first link provides a basic overview of BLEU and the second points out some problems with BLEU in terms of it's limitations.
http://kv-emptypages.blogspot.com/2010/03/need-for-automated-quality-measurement.html
and
http://kv-emptypages.blogspot.com/2010/03/problems-with-bleu-and-new-translation.html
There is also some very specific pragmatic advice on how to develop a useful Test Set at this link: AsiaOnline.Net site in the November newsletter. I am unable to put this link in as there is a limit of two.
I'd suggest refining your question. There are a great many metrics for machine translation, and it depends on what you're trying to do. In your case, I believe the problem is simply stated as: "Given a set of queries in language L1, how can I measure the quality of the translations into L2, in a web search context?"
This is basically cross-language information retrieval.
What's important to realize here is that you don't actually care about providing the user with the translation of the query: you want to get them the results that they would have gotten from a good translation of the query.
To that end, you can simply measure the discrepancy of the results lists between a gold translation and the result of your system. There are many metrics for rank correlation, set overlap, etc., that you can use. The point is that you need not judge each and every translation, but just evaluate whether the automatic translation gives you the same results as a human translation.
As for people proposing bad translations, you can assess whether the putative gold standard candidates have similar results lists (i.e. given 3 manual translations do they agree in results? If not, use the 2 that most overlap). If so, then these are effectively synonyms from the IR perspective.
In our MT Evaluation we use hLEPOR score (see the slides for details)
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).
A few associates and myself are starting an EMR project (Electronic Medical Records). I have heard talk in the past - and more so lately - about a standard record format - to facilitate the transferring of records when appropriate (HIPAA) from one facility to another. Has anyone seen any information on this?
You can look to HL7 for interoperability between systems (http://www.hl7.org/). Patient demographic information and textual notes can be passed. I've been out of the EMR space too long to know if any standards groups have done anything interesting of late. A standard format that maintains semantic meaning is a really, really difficult problem. See SnoMed (http://www.nlm.nih.gov/research/umls/Snomed/snomed_main.html) for one long-running ontology effort -- barely the start of a rich interchange format.
A word of warning from someone who spent several years with an upstart EMR vendor...This is a very hard business to be in. Sales cycles for large health systems literally can take years, and the amount of hand-holding required for smaller medical practices can quickly erode margins. Integration with existing practice management systems is non-standard, even if those vendors claim otherwise. More and more issues abound. I'm not sure that it's a wise space for an unfunded start-up to enter.
I think it's an error to consider HL7 to be a standard in the sense you seem to mean. It is heavily customized and can be quite different from one customer to the next. It's one of those standards with too much flexibility.
I recommend you read the standard (which should take you a while), then try to find a community of developers working with the standard. Ask them for horror stories, and be prepared for what you'll hear.
A month late, but...
The standard to shoot for is definitely HL7. It is used in many fields, so is highly customizable but there is a well defined standard for healthcare. Each message (ACK, DSR MCF), segment (PID, PV1, OBR, MSH, etc), sequence and event type (A08, A12, A36) has a specific meaning regardless of your system of choice.
We haven't had a problem interfacing MiSYS, Statlan, Oacis, Epic, MUSE, GE Centricity/Lastword and others sending DICOM, ADT, PACS information between the systems we have in use. Most of these systems will be set up with an interface engine to tweak messages where needed, so adding a way to filter HL7 messages as they come through to your system, and as they go out to the downstreams, would be a must.
Even if there would be a new "presidential standard" for interoperability, and I would hazard a guess that it will be HL7 anyway, I would build the system with HL7 messaging as this is currently the industry accepted standard.
While solving interoperability, you shouldn't care only about the interchange format, the local storage formats should be standardized also, to simplify the transformation to the interchange format and vice versa.
openEHR is a great format for storage, it is more expressive than HL7 v2, v3 and CDA, so it can be transformed easily to any of those. The specs are open and here: http://openehr.org/programs/specification/releases/1.0.2
For the interchange format, any of HL7 v2, v3 and CDA are good. Also consider CCR and CCD.
http://www.aafp.org/practice-management/health-it/astm.html
If you want to go outside HL7 thinking and are looking for an comprehensive EMR or EHR with a specified record format rather than a record extract message interchange format, then have a look at openEHR, http://www.openehr.org/. The ISO 13606 extract standard is (almost) a subset of openEHR. You will also find open source reference libraries and openEHR implementations of different maturity available in Java, .NET, Ruby, Python, Groovy etc.
Some organisations are also producing HL7 artifacts like CDA as output from openEHR based EHR/EMR systems.
Have a look at the Continuity of Care Record--IIRC, that's what Google Health uses for input. It's not an HL7-family standard (there's a competing HL7-family standard--don't recall what it's called off-top).
There likely will not be a standard medical record format until the government dictates the format of one and requires its use by force of law.
That almost assuredly will not happen without socialized national health care. So in reality zero chance.
its correct answer but i think some add about meaningful use of emr..... Officials Announce ‘Meaningful Use,’ EHR Certification Criteria
Last week, CMS released proposed regulations defining the “meaningful use” of electronic health records, Reuters reports (Wutkowski/Heavey, Reuters, 12/31/09).
In addition, the Office of the National Coordinator for Health IT released an interim final rule describing the required certification standards for EHR technology (Simmons, HealthLeaders Media, 12/31/09).
Under the 2009 federal economic stimulus package, health care providers who demonstrate meaningful use of certified EHRs will qualify for incentive payments through Medicaid and Medicare.
Officials will offer a 60-day public comment period after both regulations are published in the Federal Register on Jan. 13. The interim final rule on EHR certification is scheduled to take effect 30 days after publication (Goedert, Health Data Management, 12/30/09). http://www.myemrstimulus.com/
This is a very hard problem because data collection starts with an MD and the only coding they know (ICD and CPT) is all about billing, not anything likely to be of use between providers (esp. in a form where the MD can be held legally liable). And they hate even that much paperwork.
Add to that the fact that HIPAA dictates that the patient not the provider owns the data. Not that they could understand it or do anything useful with it if they had it.
Good luck. Whatever happens will result from coercion by the govt and be a long long time coming IMHO.
Interestingly the one source of solid medical info turns out to be the VA (because they don't have the adversarial issues of payment and legal liability.) Go figure. That might be a good place to start for a standard with any existing data and some momentum, though. Here's another question with some info.