Enhancement of Classifiers in HTM-CLA Using Similarity Evaluation Methods

The recent development in the theory of Hierarchical Temporal Memory (HTM) - Cortical Learning Algorithms (CLA) which models the structural and algorithmic properties of neocortex has brought in a new paradigm in the field of machine intelligence. As the theory of HTM-CLA continues to evolve, the ways of inferring the patterns and structures recognized by the HTM-CLA algorithm are still a big challenge. Moreover, the existing methods used to infer the classification output from HTM-CLA are far from satisfactory. In this paper, we propose two new classifiers using similarity evaluation methods based on dot similarity (H-DS) and mean-shift clustering (H-MSC) to obtain classification from the sparse distributed representation (SDR) output of HTM-CLA. We validate and benchmark the performance of our proposed classifiers using three datasets from the UCI machine learning repository. The results show that the proposed classifiers enhance the classification performance of HTM-CLA and their performance is also comparable to other traditional machine learning technique such as decision tree