Does KEEL require assigning weights to input variables?
A key component to judgmental decisions is an evaluation of the importance of information. There are several ways to assign weights. First all of our inputs and outputs are normalized to values between 0 and 100 (floating point or integer) depending on your target application. These weights can be defined externally to the KEEL engine. They can also be scaled internal to the KEEL engine. When combining data items inside of a KEEL engine, a single piece of information may impact different parts of the problem space with different levels of intensity. KEEL handles this as well. Another key point in a KEEL solution is that information can change in importance (to different parts of the problem) dynamically. The concept of importance is critical to many KEEL applications. The importance of data can be set at design time (fixed) and/or it can be changed dynamically based on other influencing inputs.
