Methods and algorithms of collective recognition

The collective decision making and, in particular, the collective recognition is treated as the problem of joint application of multiple classifier decisions. The decisions are made about the class of an entity, situation, image, etc. The joint decision is used to improve quality of the final decision by aggregation and coordination of different classifier decisions using a metalevel algorithm. The studies in the field of collective recognition, which were started in the middle of the 1950s, find wide application in practice during the last decade. Since they are used for solving complex large-scale applied problems, the interest of both theoretical scientists and engineers is focused on them. A new impetus for the studies was given by the recent development of embedded distributed structures involving ensembles of intellectual sensors that make decisions under uncertainties on the base of limited local information. The final decision of high quality, in particular, the decision of higher aggregation level, is made by combining local classifier decisions on the metalevel. There are dozens of recent publications proposing new ideas and new approaches and algorithms of collective recognition. Unfortunately, some papers rediscover results publushed several decades before. The goal of this review is to present the main ideas of collective recognition and to outline the status of researches basing on the original source works. The review covers the period from the 1950s, when the first ideas and methods appeared, up to present time.