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

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. Keywords—contradiction resolution; selfand outer-evaluation; visualization; self-organizing maps; dependent input neuron selection