Intro to Class Blending

I thought I'd devote the next few blogs to a concept that has gotten much less attention than it deserves: blended classes. Class blending lurks behind much of the irreproducibility in "Big Science" research, including clinical trials. It also is responsible for impeding progress in various disciplines of science, particularly the natural sciences, where classification is of utmost importance. We'll see that the scientific literature is rife with research of dubious quality, based on poorly designed classifications and blended classes. For today, let's start with a definition and one example. We'll discuss many more specific examples in future blogs. Blended class - Also known as class noise, subsumes the more familiar, but less precise term, "Labeling error." Blended class refers to inaccuracies (e.g., misleading results) introduced in the analysis of data due to errors in class assignments (i.e., assigning a data object to class A when the object should have been assigned to class B). If you are testing the effectiveness of an antibiotic on a class of people with bacterial pneumonia, the accuracy of your results will be forfeit when your study population includes subjects with viral pneumonia, or smoking-related lung damage. Errors induced by blending classes are often overlooked by data analysts who incorrectly assume that the experiment was designed to ensure that each data group is composed of a uniform and representative population. A common source of class blendi...
Source: Specified Life - Category: Information Technology Tags: classification complexity data science irreproducible results ontologies ontology Source Type: blogs