1. MOTIVATION

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Typically, a ML model is unidirectional

Given

what's the value of ?

X

Y

Idealized scenario: if you do not know the exact task on beforehand, this paradigm breaks down.
E.g.: anomaly detection (e.g. on sensor data), prediction in spreadsheets, ...

Disadvantage

f_{UD}:

X

\rightarrow

Y

Motivation

A typical ML model can solve this

Given

what's the value of ?

X

Y

Idealized scenario: if you do not know the exact task on beforehand, this paradigm breaks down.
E.g.: anomaly detection (e.g. on sensor data), prediction in spreadsheets, ...

Disadvantage

Our solution

f_1:

X

\rightarrow

Y

f_2:

X

\rightarrow

Y

f_3:

X

\rightarrow

Y

MERCS model

Compact representation:

f_1

f_2

f_3:

f_3

2. OBJECTIVES

3. MERCS MODEL

4. PREDICTION PROBLEM

5. ATTRIBUTE IMPORTANCE

6. CHAINING

7. RESULTS

8. CONCLUSIONS

MERCS-poster-content

By eliavw

MERCS-poster-content

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eliavw