A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
Abstract
This dissertation addresses a problem found in supervised ML classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The QMA is modeled after two cognitive theories: Stanovich's framework, which proposes learning results from interactions between conscious and unconscious processes; and, the IIT, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows for retaining and reasoning-over data sets in a non-ontological, non-hierarchical QS. This novel computational approach supports concept drift, by allowing the target variable to change ad infinitum without re-training while achieving classification accuracy comparable to or greater than benchmark classifiers. Additionally, the research produced a functioning model of Stanovich's framework, and acomputationally tractable working solution for a representation of qualia, which when exposed to new examples, is able to match the causal structure and generate new inferences.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2016
- Accession Number
- AD1017889
Entities
People
- Sandra L. Vaughan