CDI Type I: Collaborative Research: Machine Learning in Taxonomic Research

智力价值据估计，世界上只有不到10%的物种被描述过，但由于人类对自然栖息地的破坏，物种每天都在消失。考虑到栖息地破坏的速度很快，专家们担心，许多物种在被发现和正式描述之前就会灭绝。由于执业分类学家数量的减少和传统分类学研究的缓慢步伐，描述地球上剩余物种的工作变得更加困难。在描述新的动物物种时，分类学家通常依赖于保存在自然历史博物馆的标本。他们必须对已知物种和新发现物种的地理范围内的多个种群的大量标本进行仔细的计数和测量，以便将新物种与所有已知的近亲区分开来。这个过程很费力，可能需要几年甚至几十年的时间才能完成，具体取决于物种的地理范围。在这个项目中，研究团队将为分类学研究开发新的机器学习方法，具体目的是从根本上加快分类学修订的步伐。科学研究将集中在两个领域：物种鉴定和新物种发现。在区分一个物种与其他物种时，分类学家必须确定一组诊断特征，以区分该物种与其所有已知近缘物种。为了自动化和加快这一费力的过程，该团队将探索现有的特征子集选择技术，并将开发新的技术。分类标本的图像将用于训练代表已知的生物分类分类的统计模型的集合。将自动识别一组“最佳”的体型字符。新物种发现是分类学最重要的研究目标。从机器学习的角度来看，检测新物种与识别已知物种的问题是根本不同的，因为根据定义，训练集不包含新物种的先验知识。研究小组将把新物种发现作为新颖性检测问题来制定，并将为分类学任务开发一个高效的新颖性检测框架。广泛影响该项目如果成功实施，将展示融合不同领域的技术的成果。在生物学方面，该项目将展示机器学习技术可以帮助分类学家和进化生物学家完成各种研究任务，从而从根本上加快分类修订的步伐。在计算机科学方面，研究人员将从现实世界生物学问题和项目中创建的数据库所引发的计算挑战中受益。将开发新的机器学习算法来影响分类学研究。PI将把这项研究与他们在密西西比大学和杜兰大学的教育活动结合起来。私人投资机构将投入更多努力，指导参与探索尖端跨学科研究的女性和代表性不足的少数族裔学生。

Intellectual MeritIt is estimated that less than 10 percent of the world's species have been described, yet species are being lost daily due to human destruction of natural habitats. Considering the fast pace of habitat destruction, experts fear that many species will become extinct before they can be discovered and formally described. The job of describing the earth's remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In describing new species of animals, taxonomists typically rely on specimens deposited in natural history museums. They have to make careful counts and measurements on large numbers of specimens from multiple populations across the geographic ranges of both known and newly discovered species, in order to diagnose the new species as distinct from all of its known relatives. The process is laborious and can take years or even decades to complete, depending on the geographic range of the species. In this project, the research team will develop new machine learning methods for taxonomic research, with the specific aim of fundamentally increasing the pace of taxonomic revision.The scientific research will focus on two areas: species identification and new species discovery. In distinguishing a species from others, taxonomists must identify a set of diagnostic characters that distinguishes the species in question from all of its known relatives. To automate and expedite this laborious process, the team will explore existing feature subset selection techniques, and will develop new ones. Images of categorized specimens will be used to train a collection of statistical models representing the known taxonomic grouping of organisms. An "optimal" set of body shape characters will be automatically identified. New species discovery is the most important research objective in taxonomy. From a machine learning point of view, detecting new species is fundamentally different from the problem of recognizing known species because by definition, the training set does not contain prior knowledge of a new species. The research team will formulate new species discovery as a novelty detection problem, and will develop an efficient novelty detection framework for taxonomic tasks.Broader ImpactThe project, if carried out successfully, will demonstrate the fruitfulness of fusing technologies from different fields. From the biology side, the project will demonstrate that machine learning techniques can assist taxonomists and evolutionary biologists in various research tasks, hence fundamentally accelerate the pace of taxonomic revision. From the computer science side, researchers will benefit from the computational challenges motivated from real-world biological problems and the database created in the project. New machine learning algorithms will be developed to impact taxonomic research. The PIs will integrate the research with their educational activities at both University of Mississippi and Tulane University. The PIs will devote additional efforts to mentoring female and underrepresented minority students involved in exploring cutting-edge interdisciplinary research.