Does fuzzy logic really improve simple machine learning algorithms?

Question

I'm reading about fuzzy logic and I just don't see how it would possibly improve machine learning algorithms in most instances (which it seems to be applied to relatively often).

Take for example, k nearest neighbors. If you have a bunch a bunch of attributes like color: [red,blue,green,orange], temperature: [real number], shape: [round, square, triangle], you can't really fuzzify any of these except for the real numbered attribute (please correct me if I'm wrong), and I don't see how this can improve anything more than bucketing things together.

How can machine fuzzy logic be used to improve machine learning? The toy examples you'll find on most websites don't seem to be all that applicable, most of the time.

Answer 1

Fuzzy logic is advisable when the variables have a natural shape interpretation. For example, [very few, few, many, very many] have a nice overlapping trapezoid interpretation of values.

Variables like color might not. Fuzzy variables denote degree of membership, that's when they become useful.

Regarding machine learning, it depends on what stage of the algorithm you want to apply fuzzy logic. It would be better applied in my opinion after the clusters are found (using traditional learning techniques) to determining the degree of membership of a certain point in the search space on each cluster, but that doesn't improve learning per see, but classification after learning.