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Andrei Konstantinov, Lev Utkin and Vladimir Muliukha
A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to the w...
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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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José Manuel Porras, Juan Alfonso Lara, Cristóbal Romero and Sebastián Ventura
Predicting student dropout is a crucial task in online education. Traditionally, each educational entity (institution, university, faculty, department, etc.) creates and uses its own prediction model starting from its own data. However, that approach is ...
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Yisha Wang, Gang Yang and Hao Lu
Rapid and accurate tree-crown detection is significant to forestry management and precision forestry. In the past few decades, the development and maturity of remote sensing technology has created more convenience for tree-crown detection and planting ma...
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Apichat Suratanee and Kitiporn Plaimas
Identifying genes associated with autism spectrum disorder (ASD) is crucial for understanding the underlying mechanisms of the disorder. However, ASD is a complex condition involving multiple mechanisms, and this has resulted in an unclear understanding ...
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