TY - GEN
T1 - MCDD
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
AU - Chandrashekar, Mayanka
AU - Lee, Yugyung
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - A parallel and distributed machine learning framework are in need to deal with a large amount of data. We have seen unsatisfactory classification performance especially with increasing the number of classes. In this paper, we propose a distributed deep learning framework, called Multi-Class Discriminative Distribution (MCDD) that aims to distribute classes while improving the accuracy performance of the deep learning models with large scale datasets. The MCDD framework works on an evidence-based learning model for the optimal distribution of classes by computing a misclassification cost (i.e., confusion factor). These observations about learning attempts have been used to extend a classifier into a classification model hierarchy by learning an optimal distribution of classes. As a result, a distributed deep neural network model with multi-class classifiers (MCDD) was built to optimize the accuracy and performance of the learning process. The MCDD model runs on parallel environments, such as Apache Spark and Tensor Flow using large real-world datasets (Caltech-101, CIFAR-100, ImageNet-1K) showing that MCDD can build a class distribution model with higher accuracy compared to existing models.
AB - A parallel and distributed machine learning framework are in need to deal with a large amount of data. We have seen unsatisfactory classification performance especially with increasing the number of classes. In this paper, we propose a distributed deep learning framework, called Multi-Class Discriminative Distribution (MCDD) that aims to distribute classes while improving the accuracy performance of the deep learning models with large scale datasets. The MCDD framework works on an evidence-based learning model for the optimal distribution of classes by computing a misclassification cost (i.e., confusion factor). These observations about learning attempts have been used to extend a classifier into a classification model hierarchy by learning an optimal distribution of classes. As a result, a distributed deep neural network model with multi-class classifiers (MCDD) was built to optimize the accuracy and performance of the learning process. The MCDD model runs on parallel environments, such as Apache Spark and Tensor Flow using large real-world datasets (Caltech-101, CIFAR-100, ImageNet-1K) showing that MCDD can build a class distribution model with higher accuracy compared to existing models.
KW - Ensemble Model
KW - Large-scala Classification
KW - Parallel Distribution Mechanism
UR - https://www.scopus.com/pages/publications/85062590374
U2 - 10.1109/BigData.2018.8622438
DO - 10.1109/BigData.2018.8622438
M3 - Conference contribution
AN - SCOPUS:85062590374
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 4906
EP - 4914
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2018 through 13 December 2018
ER -