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rule-extraction algorithms for verify the Artificial Neural Network (ANN)

AngeloF88

Hello,

I recently start to use JMP Pro also for the artificial neural network.

I would like to know, how can I verify the neural network architectures with JMP?
E.g., using the mathematical expressions, symbolic logic, fuzzy logic, or decision trees...
I read that this system could resolve the "black box."

Thank you so much,
Angelo

Accepted answers

Mark_Bailey

After you run a model, you can click the red triangle at the top of the outline for that model. You have choices to view the network diagram and you can also show the estimates. The structure of each node depends on your choice of the activation function.

What else do you need?

Mark_Bailey

This quote is from the help portion about the prediction profiler command to output a random table:

"Prompts for a number of runs and creates an output table with that many rows, with random factor settings and predicted values over those settings. This is equivalent to (but much simpler than) opening the Simulator, resetting all the factors to a random uniform distribution, then simulating output. This command is similar to Output Grid Table, except it results in a random table rather than a sequenced one.

The prime reason to make uniform random factor tables is to explore the factor space in a multivariate way using graphical queries. This technique is called Filtered Monte Carlo.

Suppose you want to see the locus of all factor settings that produce a given range to desirable response settings. By selecting and hiding the points that do not qualify (using graphical brushing or the Data Filter), you see the possibilities of what is left: the opportunity space yielding the result that you want.

Some rows might appear selected and marked with a red dot. These represent the points on the multivariate desirability Pareto Frontier - the points that are not dominated by other points with respect to the desirability of all the factors."

So it is a simple way to obtain a uniform random simulation of the predictors and the model prediction of the response. It has the same purpose as the Output Grid Table command.

The Simulator function gives you much more control over the nature of the variation of the predictors and additional random variation of the response. It is primarily for assessing process capability but obviously has other applications.

All comments

Mark_Bailey

What else do you need?

AngeloF88

Thank you so much Markbailey,

Too easy for quickly found ...

Satisfied with the JMP software and its support.

Do you know if there are some publication or conference paper that describe the Estimates of ANN in JMP? Because for publicating the data, I have to support my analysis and my publication with the correct reference/s based on JMP.

Regards,

Angelo

Mark_Bailey

You might search for such references. I don't have any at hand.

Commercial software vendors do not generally publish their numerical methods or code because it is proprietary intellectual property. JMP offers this page as a resource to users to help them establish the quality of JMP.

Mark_Bailey

I have further information from JMP Development about your inquiry. They say that it is difficult to verify neural networks (NN) from any software product. NN are not like linear regression where the coefficients and predictions will be the same no matter what product you use. Every product will be very different. The best you can do is use crossvalidation to assess the predictive performance of the data mining models. If there is a strong need to verify the models that are used then stay with least squares and logistic regression.

I included a JMP white paper that addresses as much as we can say about NN in JMP.

JMP Neural Network Methodology.pdf

AngeloF88

Thank you so much! I read the paper, and it is interesting. I also read some publication (also in the journal with high impact factor) concerning the use of ANN in the plant physiological field. I believe that they are a strong predictive strument, but the problem of the black box is hard to exlicate at researchers that not work all day with mathematical. However, JMP helps to use the artificial neural network and understand the result. After your suggestion concerning the profile and estimation, I have a vision more clear of Artificial Neural Network. I read the excellent books directly available from JMP. I also found in ANN and other statistic analysis the Montecarlo simulation from the profile. Bingo!! Sometimes, this is an important simulation for paleoclimatology and in general people that work with prediction in ecology. Is very easy to do this simulation in JMP. If you know, what is in JMP the difference, between a random tables and simulation from the profile?, are bolt two type of Montecarlo simulation?

Mark_Bailey

@AngeloF88 wrote:
Is very easy to do this simulation in JMP. If you know, what is in JMP the difference, between a random tables and simulation from the profile?, are bolt two type of Montecarlo simulation?

Please clarify the tables ("random tables" and "simulation from profile") to which you are referring before I can answer your question.

AngeloF88

Hi,

Thank you so much for reply me!

I read the JMP documentation, section prediction profiler Options. And I found that "Output random table" is the filtered Montecarlo technique, while the "Simulation" is just a Montecarlo simulation that uses the random noise.

I do not know the difference between the two techniques and what method is better, I have to study better this two difference. If you have some advice , it is well appreciated. However, I have checked that Montecarlo simulation takes more time than filtered Montecarlo technique.

I have to do this in the profile of the neural network. Therefore I thought that it could be a Hybrid ANN-Montecarlo simulation.

To help to understand:

For random table,

1. I selected the option neural

2. I clicked the red triangle in the model

3. I clicked the option Prediction profile

4. I clicked the red triangle in prediction profile.

5. I selected Output random tables.

For simulation, I performed the same procedure, but instead of clicking the "Output random tables", I selected the option "simulator". I clicked the red triangle and selected "Simulation Experiment".

I adjusted the noise in the interactive cells and selected the random variable.

Marco1

Hello Mark,

I am pleased to greet you, I am new to JMP, I have some questions:

1. How can you make predictions of future stock prices with JMP neural networks?
Apparently JMP does not have the recurrent neural networks with RNN memory.
2. JMP in the training of the model optimizes the weights of the networks with genetic algorithms or genetic programming?
3. JMP optimizes the number of neurons and the number of hidden layers with genetic algorithms or genetic programming?

I have tried JMP ARIMA to predict future stock price with poor results.

Greetings,

Marco

Dan_Obermiller

This probably should be a new thread as it does not directly relate to the original questions. I'm also not Mark, but I think I can answer your questions.

1. In order to predict a future event, you would need to introduce a time element to your neural network. There are multiple ways to do this. One way is to lag the target variable by one time period. So if your time interval is days, you could use yesterday's inputs to predict today's target. You are correct that JMP does not offer recurrent neural networks.

2. JMP does not use genetic algorithms or genetic programming in the optimization of the weights.

3. No, JMP does not use genetic algorithms or genetic programming in optimizing the number of neurons or number of hidden layers. In fact, JMP does not optimize the number of neurons or the choice of one or two hidden layers. JMP offers an exhaustive search for all of the various settings you wish to attempt. You can then choose the winner.

For these more advanced neural network features, you might want to consider SAS as these techniques are available in SAS as well as Viya.

Finally, please keep in mind that there have been lots of studies of trying to predict stock prices. Studies have shown that short-term stock prices are best modeled as a "random walk" meaning that there is no discernible pattern. In other words, don't blame the modeling technique for not working on a set of data that cannot be modeled well. Good luck!

Mark_Bailey

Thank you, @Dan_Obermiller, for answering Marco's questions. I would also like to add the white paper by Chris Gotwalt, who manages the JMP statistical developers group.

JMP Neural Network Methodology.pdf

Marco1

Hello dan,

I am glad to greet you, JMP seems to me to be an excellent software and every day it surprises me to discover the solution options it offers ..... some questions:

1. The answer of point 1 is interesting (retrace the target variable ... there are several ways to do this), you could detail with examples.
2. I have noticed that the impulse finds the optimal quantity, will there be an option in the neural network platform that optimizes the parameters or hyper-parameters of 1 model? ... that is, learning rate, optimal number of neurons, optimal number layers ... etc

Greetings,

Marco