ABSTRACT: This paper proposes a structured data prediction method based on Large Language Models with In-Context Learning (LLM-ICL). The method designs sample selection strategies to choose samples ...

Abstract: An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies ...

ABSTRACT: The objective of this work is to determine the true owner of a land—public or private—in the region of Kumasi (Ghana). For this purpose, we applied different machine learning methods to the ...

Abstract: The K-nearest neighbors (kNNs) algorithm, a cornerstone of supervised learning, relies on similarity measures constrained by real-number-based distance metrics. A critical limitation of ...

Malnutrition among children in third-world countries like the Philippines remains a critical issue addressed by the UN’s Zero Hunger goal. Traditional methods such as K-Nearest Neighbor face ...

Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting ...