Target class classification is a mixed classification and transition model whose integrated goal is to assign objects to a certain, so called target or normal class. The classification process is iterative, and in each step an object in a certain class undergoes an action attached to that class, initiating the transition of the object to one of the classes. The sequence of transitions, which we call class transitions, must be designed to provide the final assignment of objects to the target class. The transition process can be described in the form of a directed graph, and the success of the final classification is mainly due to the properties of this graph. In our previous research we showed that the desirable structure of the transition graph is an oriented rooted tree with orientation towards the root vertex, which corresponds to the normal class. It is clear that the transition graph of an arbitrary algorithm (policy) may not have this property. In this paper we study the structure of realistic transition graphs, which makes it possible to find classification inconsistencies, helping to transfer it into the desired form. The medical interpretation of dynamic treatment regime considered in the article further clarifies the investigated framework.

翻译：目标类别分类是一种混合分类与转移模型，其整体目标是将对象分配至某一指定的所谓目标或正常类别。分类过程是迭代的，每一步中，处于某一类别的对象会经历该类别对应的操作，从而触发对象向其他类别的转移。我们将这些转移序列称为类别转移，必须设计这些转移以实现对象最终归属至目标类别。该转移过程可用有向图描述，而最终分类的成功与否主要取决于该图的性质。我们先前的研究表明，转移图理想的结构是以根顶点为指向根顶点的有向根树，其中根顶点对应正常类别。显然，任意算法（策略）的转移图可能不具备此性质。本文研究实际转移图的结构，这使得能够发现分类不一致性，从而帮助将其转换为理想形式。文中考虑的动态治疗方案的医学解释进一步阐明了所研究的框架。