Complexity of the CFP, a method for Classification based on Feature Partitioning
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TL;DR
This paper presents a new methodology for learning from examples, called Classification by Feature Partitioning (CFP), by storing the objects separately in each feature dimension as disjoint partitions of values.
Abstract
This paper presents a new methodology for learning from examples, called Classification by Feature Partitioning (CFP). Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition is expanded through generalization or specialized by subdividing it into sub-partitions. It is shown that the CFP algorithm has a low sample and training complexity.