Theory of electoral college framework-based multi-classifier ensembling and its applications to subjective pattern recognition

本文的研究将应用于一类模式识别的应用，我们称之为主观模式识别，人脸识别和基于内容的信息检索作为典型的例子。我们对大脑如何处理这类信息知之甚少;事实上，判断相似性/不相似性的标准是主观的-通常需要人工来验证机器得出的任何结论。虽然每年都有新的算法被开发出来，但大多数研究都遵循一个通用的“试错”过程：给定一个数据集，对于一个已知的算法，我们尝试各种参数;如果我们对准确性不满意，我们尝试一个新的算法;然后对于新算法，我们尝试各种参数;所提出的研究将导致用于主观模式识别的新型算法，其具有可预测的高稳定性，因此保证在高水平上执行（即，准确性）。 这项研究与选举团（EC）密切相关。选举委员会的投票形式已经在政治选举中使用了很多年：一个国家被划分为几个地区;每个地区的获胜者由其投票人口的大多数决定;最终获胜者是根据每个候选人获胜地区的加权和根据赢家通吃原则选出的。它也被用于许多科学研究领域。本研究试图开发一个模型，我们所谓的EC框架为基础的多分类器集成，以改善常规EC格式，一般的决策方法，而不是简单的投票计数将用于在每个区域的本地决策，先进的集成技术，而不是简单的赢家通吃规则将用于组合的本地决策到最终的。 在理论方面，我们的模型将是第一个集成分类器集成技术与EC框架，它也将是第一个说明的EC框架为基础的多分类器集成的稳定性和适用性。建议的研究将建立采用我们的模型在模式识别应用程序，特别是在主观模式识别的指导方针。

The proposed research will be applied to a class of pattern recognition applications, which we call subjective pattern recognition, with face identification and content-based information retrieval as typical examples. We know very little about how our brains process this type of information; indeed, the standards for judging the similarity/dissimilarity are subjective -- manpower is usually required to verify any conclusions made by machine. Although new algorithms are developed every year, much of the research follows a general "trial-and-error" procedure: Given a data set, for a known algorithm, we try various parameters; if we are not satisfied with the accuracy, we try a new algorithm; and then for the new algorithm, we try various parameters; and so on. The proposed research will lead to a new type of algorithm for subjective pattern recognition, which has predictable high stability and therefore is guaranteed to perform (i.e. accuracy) at a high level. The research proposed is closely related to the Electoral College (EC). The EC voting format has been used for many years in political elections: a nation is partitioned into regions; the winner of each region is determined by a majority of its voting population; the final winner is selected according to the weighted sum of each candidate's winning regions based on the winner-take-all principle. It has also been used in many areas of scientific research. This research attempts to develop a model, our so-called EC framework-based multi- classifier ensembling, to improve the regular EC format in that general decision making approaches rather than simple vote counting will be used in each region for local decision making, and advanced ensembling technology rather than simple winner-take-all rule will be used for combining local decisions into final ones. On the theoretical front, our model will be the first that integrates classifier ensembling techniques with the EC framework; it will also be the first that illustrates the stabilities and applicabilities of the EC framework- based multi-classifier ensembles. The proposed research will establish guidelines for adopting our model in pattern recognition applications, particularly in subjective pattern recognition.