Work

Improving the Imbalanced Data Accuracy Using CNN and ReLU

Year: 2024

Type: article

Abstract: In today’s academic world, learning from datasets is a trendy topic. In the event of imbalanced data, none of the many data mining tools available for this purpose work well, partly because this type of data generates a range of minority classes, which might obstruct the learning process. In addition to its enormous volume, big data has the traits of speed and variety. In this paper, the authors have proposed a CNN model with a rectified linear activation function (ReLU) activation function to get good accuracy on an imbalanced dataset. The dataset used in this paper was electrocardiogram (ECG) heartbeat categorization.

Source: IETI Transactions on Data Analysis and Forecasting (iTDAF)

DOI: 10.3991/itdaf.v2i3.51013

Authors: Adnan Saeed, Junaid Baber, Muhammad Zain Abbas, Ahthasham Sajid, Hamza Razzaq, Arsalan Ali Khan

Institutions: University of Balochistan, Riphah International University

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4403545760

FWCI: 0.304

Cites:

Cited by: 2

Related to: 10