I can't help but think that the reason why randomization appear to add information in the context of machine learning classifiers for the dichotomous case is that the actual evaluation of the algorithms is empirical, and, as such, use finite data. Two classes of non-demonic error always exist in the finite case. These are measurement error, and sampling error.
Multiattribute classifiers use individual measurements from many attributes. No empirical measurement is made without error; the more attributes that are measured, the more measurement error intrudes.
No finite sample is an exact sample of the entire population. To the measurement error for each attribute, we must add sampling error.... (read more)
I can't help but think that the reason why randomization appear to add information in the context of machine learning classifiers for the dichotomous case is that the actual evaluation of the algorithms is empirical, and, as such, use finite data. Two classes of non-demonic error always exist in the finite case. These are measurement error, and sampling error.
Multiattribute classifiers use individual measurements from many attributes. No empirical measurement is made without error; the more attributes that are measured, the more measurement error intrudes.
No finite sample is an exact sample of the entire population. To the measurement error for each attribute, we must add sampling error.... (read more)