CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification

1 code implementation • 12 Dec 2017 • Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid

We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios.

Clustering Ensemble Learning +2

MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification

no code implementations • 18 Dec 2017 • Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman

The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.

BIG-bench Machine Learning Classification +1

LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification

no code implementations • 15 Nov 2017 • Sajid Ahmed, Farshid Rayhan, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman

The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning.

Classification General Classification

FRnet-DTI: Deep Convolutional Neural Networks with Evolutionary and Structural Features for Drug-Target Interaction

no code implementations • 19 Jun 2018 • Farshid Rayhan, Sajid Ahmed, Zaynab Mousavian, Dewan Md. Farid, Swakkhar Shatabda

In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction.