Thanks for the useful replies, I really appreciate that!
@Ted and Hector: My initial parameters (predictors) are blood
pressures, heart rates, etc: they come every minute from a patient's
monitor.
In my implementation, I plan refering to this Paper :
http://www.multi-science.co.uk/acce-free.pdf on page 7 (Table 1) you
can see the parameters used. On page 17, figure 4 you can see
vizualization of the prediction using time series:
I think I still plan using the logistic regression implementation
(since I am already worked into it), but I am confuzed how to
implement time series with Mahout. Should I create periodically (for
example every 15 minutes) a new logistic regression model, in order to
predict the probability of instability? Then the amount of training
data depends on the 'time window for the past' that I will be using.
For example, for data only two hours from the past, I will have only
circa 60 * 2 = 120 examples for creating a temporal model (I assume
that I will need one compound data vector pro minute, including blood
pressures, heart rates, etc...)
Or should I implement the time series so, that I train the model only
once with old data of many patients and the training algorithm will be
so, that it checks what is the patient's hemodynamic stability in two
hours (since this information is also known during the training)? In
this case, I will potentually have many more examples (one million or
more...)
Many thanks, best regards and sorry for the long post.
Svetlomir.
Zitat von Ted Dunning <[email protected]>:
What Hector said.
You will need to extract features from your time history.
The question also comes up about how large is your data set. If it is less
than 100,000 training examples or so, then you will probably be better off
using a system like R which handles that much data easily and has
essentially every kind of classifier available for you to try.
If you have 1 million training examples or more, then Mahout begins to
dominate alternatives. Even there, Mahout is currently optimized for sparse
data which is not what you have. My guess is that using the
OnlineLogisticRegression or some of Hector's recent patches is the way to
go. The AdaptiveLogisticRegression is heavily oriented around per term
annealing and magic knob tuning in the context of sparse data.
Can you post your data?
On Sun, Jun 5, 2011 at 10:04 AM, Hector Yee <[email protected]> wrote:
You can also try HMMs:
https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/classifier/sequencelearning/hmm/package-tree.html
If you want to do it with a classifier you can window your time series and
make a training set
e.g.
label, feature
stable, (last X seconds of time series)
unstable, (last X seconds of time series)
On Sun, Jun 5, 2011 at 8:08 AM, Svetlomir Dimitrov Kasabov <
[email protected]> wrote:
> Hello,
>
> I plan using Apache Mahout's Logistic Regression (LR) implementation in
my
> Master-Thesis. We plan using time series in order to predict, whether a
> particular patient will have an instable blood flow soon or not. Thats's
why
> I want to ask you if it is possible to use Mahout in connection with time
> series ? Do you see any potential problems / risks ?
>
> Many thanks and best regards!
>
> Svetlomir Kasabov.
>
>
>
> --
> Svetlomir Dimitrov Kasabov
>
> ----------------------------------------------------------------
> This message was sent using IMP, the Internet Messaging Program.
>
>
--
Yee Yang Li Hector
http://hectorgon.blogspot.com/ (tech + travel)
http://hectorgon.com (book reviews)
--
Svetlomir Dimitrov Kasabov
----------------------------------------------------------------
This message was sent using IMP, the Internet Messaging Program.
