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When we talk about the curse of dimensionality, we often refer to the difficulty that arises when analysing and organising data in high-dimensional spaces. In this blog, I will talk about a less known problem related to decision trees, which has a lot of similarities to data analytics in high-dimensional spaces.

Update (November 2020): This presentation, in the extended form was presented at AI Session on Biotech & Pharma under the title: “Remote Patient & Elderly Care Monitoring“.

Video of the same talk is here:

Decision trees

Decision trees can be explained by this simple three-step process:

  1. Learn rules from data
  2. Apply each rule at each node
  3. Classification is at the leaves of the tree

Assume a simple example: you are asked to create a rule from two input data sources (e.g. temperature and humidity). Each data source measurement is sampled in one of three states (e.g. low, medium and high). The final decision of the reasoning process is a TRUE/FALSE statement that depends on these two inputs.

As the figure above shows, this leads to 18 leaf nodes (red/green dots) and overall 31 nodes for only two variables! We have simplified the example by assuming only subset of AND Boolean functions (see later) for a given combination. For instance, we didn’t model (X is low) OR (Y is either medium or high) -> as that would be mean connecting some other branches!

The depth of the tree grows linearly with the number of variables, but the number of branches grows exponentially with the number of states. Decision trees are useful when the number of states per variable is limited (e.g. binary YES/NO) but can become quite overwhelming when the number of states increases. In general case, for people that are interested in little bit of maths:

How many distinct decision trees do we have with ‘n’ Boolean attributes?
A = number of Boolean functions = number of distinct truth tables with 2^n rows = 2^2^n
e.g.: With 6 Boolean attributes we get 18,446,744,073,709,551,616 !!!

Boolean functions can be expressed using this table

which in the case of n=2 gives 16

More about this you can find in this Berkeley course on AI, topic on decision trees, page 11 and 12

Waylay Rules Engine vs. Decision trees

In order to show the difference between how we model rules in waylay compared to CEP and Decision trees engines, I have taken the example from this blog: “How-to: Build a Complex Event Processing App on Apache Spark and Drools“.

In this example (you can find the the code here), the author considers running CEP for sepsis-shock analysis using Spark, HBase and Drools, evaluating the conditions at runtime. Sepsis analysis is described here:

This is what the author says (part of the text put in bold by me):

“As you can see from the above picture, a patient would meet SIRS criteria if any two vitals go out of range in a 24-hour window. These vitals arrive at different times, so typically you would use HBase to re-construct a patient state every time we get a new vital reading and then run though the rules. For the sake of this example, lets assume that you are getting all the patient’s vitals in every event, and skip the snapshot/state-building step.

What is interesting is that he already mentioned one issue, which is about data coming in different time intervals. Waylay engine natively solves this problem by eviction policy on the sensor level, but for the moment, let’s stay focused at decision trees issues.

In order to solve the problem the author is using Drools rules expressed in the excel file (you can read more about how this is done at this link):

As described earlier, the excel sheet becomes incredibly big, as you can imagine from the inherited problem of branching trees. What is also not so great is that you start seeing conditions and even the code definitions (not the code itself) inside the excel sheet.

Here is how the author explains the excel, it speaks for itself:

In the above picture:

Only once all that is done, you can start the Spark, HBASE and Drools integration.

How long do you think it takes to implement this use case? How do you test it? How do you visualise rule at runtime?

Majority voting in decision trees?

One thing that often gets swept under the carpet is that decision trees have a problem of expressing majority “voting”, like in the example of SIRS, where at least 2 out of 4 conditions needed to be TRUE in order to pass the SIRS criteria.

Of course you can make this condition in the code, but then what is the point in doing this visually (or in the excel sheet). From Drools document, here is how you deal with multiple conditions:

So, in the example above, you would still need to code a rule that would trigger if at least 2 out of 4 conditions are crossed.
Also note the warning on the memory requirements for large decision tables – as you might guess it comes from branching…

Creating the same example using WAYLAY

Can we do it better? Let’s do it the Waylay way:
The Waylay engine allows compact representation of logic. Combining variables is simplified by adding aggregation nodes. This is our template created in 5 minutes, ready for testing and deployment:

This is how we have created a majority rule example, one gate in the rule for SIRS criteria (3x3x3x2=54 combinations for that gate, where one of the nodes is actually a gate – PaC02OrRespRateIssue, with 3×3 aggregate). So this gate already shows 243 combinations!

Another sub-rule which we need to add combines the current and previous measurements (Lactic acidosis, SBP <90 or SBP drop ≥ 40 mm Hg of normal):

Taking into account previous measurements was easily modelled using the function node , with expression <SBP.parameter>[-1] - <SBP.parameter> and threshold of 40, which was then combined with the threshold crossing on the current measurement. Once the rule is ready, we can easily visually inspect different conditions and check if all things are properly modelled.

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