A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
Technical Report,01 Oct 2012,15 Sep 2016
AFIT WPAFB United States
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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 Stanovichs 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 Stanovichs 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.