Softlog-Softmax Layers and Divergences Contribute to a Computationally Dependable Ensemble Learning

The paper proposes a 4-step process for highlighting that softlog-softmax cascades can improve both consistency and dependability of the next generation ensemble learning systems. The first process is anatomical in nature: the target ensemble model under consideration is composed by canonical elements relating to the definition of a convolutional frustum. No a priori is considered in the choice of canonical forms. Diversity is the main criterion for selecting these forms. It is shown that the more complex the problem, the more useful this ensemble diversity is. The second process is physiological and relates to neural engineering: a softlog is derived to both make weak logarithmic operations consistent and lead, through multiple softlog-softmax layers, to intermediate decisions in the sense of respecting the same class logic as that faced by the output layer. The third process concerns neural information theory: softlog-based entropy and divergence are proposed for the sake of constructing information measures yielding consistent values on closed intervals. These information measures are used to determine the relationships between individual and sub-community decisions in frustum diversitybased ensemble learning. The concluding process addresses the derivation of an informative performance tensor for the purpose of a reliable ensemble evaluation.