Contradiction Resolution of Competitive and Input Neurons to Improve Prediction and Visualization Performance

Ryotaro Kamimura

doi:10.14569/ijarai.2013.021206

Abstract

In this paper, we propose a new type of informationtheoretic method to resolve the contradiction observed in competitive and input neurons. For competitive neurons, contradiction between self-evaluation (individuality) and outer-evaluation (collectivity) exists, which is reduced to realize the self-organizing maps. For input neurons, there exists contradiction between the use of many and few input neurons. We try to realize a situation where as many input neurons as possible are used, and at the same time, another where only a few input neurons are used. This contradictory situation can be resolved by viewing input neurons on different levels, namely, the individual and average level. We applied contradiction resolution to two data sets, namely, the Japanese short term economy survey (Tankan) and Dollar-Yen exchange rates. In both data sets, we succeeded in improving the prediction performance. Many input neurons were used on average, but a few input neurons were only taken for each input pattern. In addition, connection weights were condensed into a small number of distinct groups for better prediction and interpretation performance.

Highlights

Though competitive and input neurons are supposed to be governed by the same parameter, β in our formulation of contradiction resolution, we have empirically found that input and competitive neurons should be controlled by different parameter rules for better prediction performance
We introduced contradiction resolution to improve the prediction and interpretation performance of neural networks
Contradiction is realized in terms of competitive neurons, input neurons and the number of input neurons

Summary

Introduction

We have so far introduced contradiction resolution for neural networks [1], [2]. We believe that neural networks can be viewed from multiple points of view. If contradiction exits between the different points of view, it should be reduced as much as possible. An example of contradiction is two types of evaluation for a neuron [2], namely, self- and outer-evaluation. In self-evaluation, a neuron is evaluated for itself without considering the other neurons. In outerevaluation, the neuron can be evaluated by all the other neurons. If contradiction between self- and outer-evaluation exists, this contradiction should be reduced as much as possible

Methods

Results

Conclusion