Saturday, June 11, 2011
Call for papers: CEC 2012
The deadline for papers submitted to the 2012 Congress on Evolutionary Computation is December 19, 2011. This conference is part of the 2012 IEEE World Congress on Computational Intelligence (WCCI 2012) and is held concurrently with the 2012 International Joint Conference on Neural Networks (IJCNN) and IEEE Conference on Fuzzy Logic (Fuzz-IEEE). WCCI 2012 will be held in Brisbane, Australia, June 10-15, 2012.
Friday, June 10, 2011
Conference paper deadline: CIBCB 2012
The deadline for papers submitted to the 2012 conference on Computational Intelligence in Bioinformatics and Computational Biology is November 20, 2011. This conference will be held in San Diego, California, May 9-12, 2012.
Tuesday, May 31, 2011
Social Internetworking
I've just put online a brief description (PDF) of how I connect this blog to other social media sites, including LinkedIn, Twitter, and ResearchGATE.
I describe it as "Social Internetworking" (not an original phrase) and it involves using various free aggregators and web services to export blog posts.
You can follow me on Twitter at https://twitter.com/#!/DrMikeWatts, where you will see updates to this blog as soon as they are posted. You can find a complete list of my social networking profiles on my personal web page, mike.watts.net.nz.
I describe it as "Social Internetworking" (not an original phrase) and it involves using various free aggregators and web services to export blog posts.
You can follow me on Twitter at https://twitter.com/#!/DrMikeWatts, where you will see updates to this blog as soon as they are posted. You can find a complete list of my social networking profiles on my personal web page, mike.watts.net.nz.
Saturday, May 28, 2011
Fuzzy Markup Language
Giovanni Acampora describes the Fuzzy Markup Language (FML) in a series of articles. FML is a XML-based method for describing fuzzy logic systems. Fields in the schema specify the fuzzy knowledge base, which consists of the fuzzy variables and their membership functions, and the fuzzy rule base. The schema also allows for the specification of the inference and defuzzification method to use, and the type of fuzzy system (Zadeh-Mamdani or Takagi-Sugeno-Kang). Finally, it supports distributed fuzzy rule systems, that is, the user can specify the IP address of machines on which parts of the fuzzy system should run.
The major advantage of using XML to describe a fuzzy system is interoperability. All that is needed to read an XML file is the appropriate schema for that file, and an XML parser. This makes it much easier to exchange fuzzy systems between software: for example, an application could extract fuzzy rules from a neural network (like the EFuNN and SECoS rule extraction algorithms that exist) which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please.
Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work.
Such standard, XML-based file formats would be useful for other areas of computational intelligence: a standard XML format for ANN, for example, would be fairly simple to implement and also very useful. I could imagine, for example, training a MLP, saving it in an XML-based format, then using XSLT to transform it to C++ and uploading it into an embedded controller. Conventional, static-architecture ANN like perceptrons, MLP, or SOM could easily be represented in XML.
I will be watching for further developments in this area of technology: I've had quite enough of designing my own file formats!
The major advantage of using XML to describe a fuzzy system is interoperability. All that is needed to read an XML file is the appropriate schema for that file, and an XML parser. This makes it much easier to exchange fuzzy systems between software: for example, an application could extract fuzzy rules from a neural network (like the EFuNN and SECoS rule extraction algorithms that exist) which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please.
Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work.
Such standard, XML-based file formats would be useful for other areas of computational intelligence: a standard XML format for ANN, for example, would be fairly simple to implement and also very useful. I could imagine, for example, training a MLP, saving it in an XML-based format, then using XSLT to transform it to C++ and uploading it into an embedded controller. Conventional, static-architecture ANN like perceptrons, MLP, or SOM could easily be represented in XML.
I will be watching for further developments in this area of technology: I've had quite enough of designing my own file formats!
Tuesday, May 24, 2011
Evolving Connectionist Systems
An interesting family of neural networks is Evolving Connectionist Systems (ECoS). These were invented by Professor Nik Kasabov around 1998. ECoS are constructive networks, that is, they do not start with a fixed structure but instead grow (add neurons) as training data is presented to them. The advantages of this are:
The first ECoS was the Evolving Fuzzy Neural Network EFuNN. Later ECoS include the Simple Evolving Connectionist System SECoS (which is really an EFuNN with the fuzzy logic elements removed) and the Evolving Clustering Method ECM. EFuNN and SECoS both have rule extraction algorithms associated with them, by which fuzzy rules can be extracted from a trained EFuNN or SECoS network. This makes ECoS very useful for data mining, especially in an online application area.
I wrote a review of ECoS technology a couple of years ago, in this paper. An online reprint is available here. I also maintain a website of resources on ECoS networks at: ecos.watts.net.nz.
Research on ECoS networks is continuing, especially at Prof. Kasabov's lab KEDRI. Nowadays, ECoS research is focused on spiking neuron models, that is, neurons that include a temporal aspect to their activation, much as biological neurons do.
- they are fast learning, as they learn the data as it presented, rather than iteratively
- they are hard to over-train, as new data is accommodated by adding new neurons to the network
The first ECoS was the Evolving Fuzzy Neural Network EFuNN. Later ECoS include the Simple Evolving Connectionist System SECoS (which is really an EFuNN with the fuzzy logic elements removed) and the Evolving Clustering Method ECM. EFuNN and SECoS both have rule extraction algorithms associated with them, by which fuzzy rules can be extracted from a trained EFuNN or SECoS network. This makes ECoS very useful for data mining, especially in an online application area.
I wrote a review of ECoS technology a couple of years ago, in this paper. An online reprint is available here. I also maintain a website of resources on ECoS networks at: ecos.watts.net.nz.
Research on ECoS networks is continuing, especially at Prof. Kasabov's lab KEDRI. Nowadays, ECoS research is focused on spiking neuron models, that is, neurons that include a temporal aspect to their activation, much as biological neurons do.
Wednesday, May 18, 2011
Modelling distribution of jellyfish with ANN
A new paper first-authored by David Pontin, my ex-PhD student from Lincoln University. This describes how he used MLP to model to presence and absence of a species of stinging jellyfish (Physalia physalis) at New Zealand beaches.
There are a couple of interesting points about this paper. Firstly, because there have been no surveys of Physalia distribution, a surrogate data set was used. This data set was stings recorded by lifeguards of Surf Lifesaving New Zealand. Since lifeguards treat jellyfish stings, each incident has to be recorded, and Physalia is the only stinging organism in New Zealand waters, a fairly large data set was available as to the presence of these jellyfish. Predictions were made from oceanic variables such as wave height and direction, and wind speed and direction.
Secondly, the data was carefully cleaned: since stings of swimmers was used as the surrogate for Physalia presence, times when there were no swimmers at the beach were excluded from the data set. While this introduced a small missing-not-at-random bias, it also removed a large number of false absences: if an example was recorded as an absence, then it was because there were no stings recorded, not because there was no one in the water.
Thirdly, an analysis of the contributions of each input of the ANN was performed. This showed which of the oceanic variables contributed the most to the presence of Physalia. This analysis indicated that there may be a hitherto unknown spawning ground for this species in the Tasman Sea.
Finally, and this is in many ways the focus of the paper, the contribution analysis of the ANN was compared with the results of input contribution analysis by an evolutionary algorithm.
Overall, this is a nice little paper that neatly sums up David's work and contributes to the understanding of the behaviour of Physalia. This shows how useful computational intelligence is to ecological applications, an area where there is, in my opinion, enormous potential for computational intelligence researchers to make real, meaningful contributions.
There are a couple of interesting points about this paper. Firstly, because there have been no surveys of Physalia distribution, a surrogate data set was used. This data set was stings recorded by lifeguards of Surf Lifesaving New Zealand. Since lifeguards treat jellyfish stings, each incident has to be recorded, and Physalia is the only stinging organism in New Zealand waters, a fairly large data set was available as to the presence of these jellyfish. Predictions were made from oceanic variables such as wave height and direction, and wind speed and direction.
Secondly, the data was carefully cleaned: since stings of swimmers was used as the surrogate for Physalia presence, times when there were no swimmers at the beach were excluded from the data set. While this introduced a small missing-not-at-random bias, it also removed a large number of false absences: if an example was recorded as an absence, then it was because there were no stings recorded, not because there was no one in the water.
Thirdly, an analysis of the contributions of each input of the ANN was performed. This showed which of the oceanic variables contributed the most to the presence of Physalia. This analysis indicated that there may be a hitherto unknown spawning ground for this species in the Tasman Sea.
Finally, and this is in many ways the focus of the paper, the contribution analysis of the ANN was compared with the results of input contribution analysis by an evolutionary algorithm.
Overall, this is a nice little paper that neatly sums up David's work and contributes to the understanding of the behaviour of Physalia. This shows how useful computational intelligence is to ecological applications, an area where there is, in my opinion, enormous potential for computational intelligence researchers to make real, meaningful contributions.
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