Southeast Asian bumblebee specimen dataset using Adaboost technique

Project Code :TCMAPY991

Objective

The Machine Learning objective of using the AdaBoost technique on the Southeast Asian bumblebee specimen dataset is to enhance classification accuracy. By combining weak classifiers into a strong one, it helps in predicting various bumblebee species or behaviors, thus contributing to understanding the diversity and ecological significance of these insects in the Southeast Asian region.

Abstract

The southeast Asian bumblebee species are facing population declines due to habitat loss, climate change, and pesticide use. accurate identification of bumblebee species is essential for their conservation and management in this study, we present a dataset of southeast Asian bumblebee specimens and use the adaboost algorithm for species classification. ad boost is a popular ensemble learning technique that combines multiple weak classifiers to create a strong classifier.
the dataset consists of images and morphological measurements of bumblebee specimens from various locations in southeast Asia. the ad boost algorithm is trained on a subset of the dataset and tested on a separate validation set. the performance of the adaboost algorithm is evaluated using metrics such as accuracy, precision, recall, and f1 score.

Keywords : Machine learning algorithms and dataset.

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