发情周期
决策树
朴素贝叶斯分类器
分类器(UML)
模式识别(心理学)
人工智能
决策树学习
计算机科学
贝叶斯定理
树(集合论)
生物
数学
支持向量机
贝叶斯概率
内分泌学
数学分析
作者
I. Maldonado-Castillo,Mark Eramian,R.A. Pierson,Jaswant Singh,Gregg P. Adams
摘要
Studies of ovarian development in female mammals have shown a relationship between the day in the estrous cycle and the size of the main structures and physiological status of the ovary. This paper presents an algorithm for the automatic classification of bovine ovaries into temporal categories using information extracted from ultrasound images. The temporal classes corresponded roughly to the metestrus, diestrus, and proestrus phases of the bovine reproductive cycle. Features based on the sizes of ovarian structures formed the patterns on which the classification was performed. A Naive Bayes classifier was able to correctly classify the stage of the estrous cycle for 86.36% of the test patterns. A decision tree classified 100% of the test patterns correctly. The decision tree inference algorithm used to build the classifier constructed a tree that used only two of the five available features indicating that they form a sufficiently rich set of features for robust classification.
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