Fuzzy Rough Choquet Distances for Classification
원문 초록
This paper introduces a novel Choquet distance using fuzzy rough set based measures. The proposed distance measure combines the attribute information received from fuzzy rough set theory with the flexibility of the Choquet integral. This approach is designed to adeptly capture non-linear relationships within the data, acknowledging the interplay of the conditional attributes towards the decision attribute and resulting in a more flexible and accurate distance. We explore its application in the context of machine learning, with a specific emphasis on distance-based classification approaches (e.g. k-nearest neighbours). The paper examines two fuzzy rough set based measures that are based on the positive region. Moreover, we explore two procedures for monotonizing the measures derived from fuzzy rough set theory, making them suitable for use with the Choquet integral, and investigate their differences.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Attribute-Based Classification for Zero-Shot Visual Object Categorization
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
Attributes Shape the Embedding Space of Face Recognition Models
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
토론
이 논문과 연결된 포럼 글이 아직 없습니다.