Transition Graph Properties of Target Class Classification
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913278284791808 |
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| author | Aslanyan, Levon Sahakyan, Hasmik |
| author_facet | Aslanyan, Levon Sahakyan, Hasmik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transition Graph Properties of Target Class Classification Aslanyan, Levon Sahakyan, Hasmik Machine Learning Artificial Intelligence Discrete Mathematics 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. |
| title | Transition Graph Properties of Target Class Classification |
| topic | Machine Learning Artificial Intelligence Discrete Mathematics |
| url | https://arxiv.org/abs/2403.15167 |