click here to download project abstract of imbalanced data machine learning
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTRACT
In the realm of supervised machine learning, imbalanced datasets present a critical challenge. This disparity emerges when the distribution of data points across various classes is uneven, notably impacting classification tasks. Often encountered in medical diagnosis, spam filtering, and fraud detection domains, imbalanced datasets feature a dominant class (majority/negative) and a less represented class (minority/positive).
Such datasets, commonly available on aggregation platforms, offer a diverse compilation of information from various sources. The absence of excessive curation ensures a more realistic representation of real-world scenarios. However, this diversity can lead to challenges in labeling and categorization.
Addressing this challenge, our project incorporates a Human Annotator system to curate datasets collected from public sources. This Human Annotator segregates the data into labeled and unlabeled sets. To access learning materials, users register and engage with these datasets.
Notably, active learning, known for its effectiveness, encounters limitations when applied directly to imbalanced datasets. Recent studies highlight the shortcomings of traditional active learning methodologies in such scenarios. Hence, our project emphasizes human annotation to meticulously analyze and match unlabeled data with appropriate labels, ensuring a more accurate and reliable dataset for learners.
As a result, the learning materials become more robust and informative, providing learners with a comprehensive educational experience. Thus By bridging the gap between imbalanced data challenges and effective learning outcomes, our platform endeavors to empower learners with a more holistic understanding of the subject matter, bolstered by a well-curated dataset that ensures a more profound grasp of the intricacies inherent in different classes within the dataset.
