Rank4Class: A Ranking Formulation for Multiclass Classification
- URL: http://arxiv.org/abs/2112.09727v1
- Date: Fri, 17 Dec 2021 19:22:37 GMT
- Title: Rank4Class: A Ranking Formulation for Multiclass Classification
- Authors: Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael
Bendersky, Marc Najork
- Abstract summary: Multiclass classification (MCC) is a fundamental machine learning problem.
We show that it is easy to boost MCC performance with a novel formulation through the lens of ranking.
- Score: 26.47229268790206
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multiclass classification (MCC) is a fundamental machine learning problem
which aims to classify each instance into one of a predefined set of classes.
Given an instance, a classification model computes a score for each class, all
of which are then used to sort the classes. The performance of a classification
model is usually measured by Top-K Accuracy/Error (e.g., K=1 or 5). In this
paper, we do not aim to propose new neural representation learning models as
most recent works do, but to show that it is easy to boost MCC performance with
a novel formulation through the lens of ranking. In particular, by viewing MCC
as to rank classes for an instance, we first argue that ranking metrics, such
as Normalized Discounted Cumulative Gain (NDCG), can be more informative than
existing Top-K metrics. We further demonstrate that the dominant neural MCC
architecture can be formulated as a neural ranking framework with a specific
set of design choices. Based on such generalization, we show that it is
straightforward and intuitive to leverage techniques from the rich information
retrieval literature to improve the MCC performance out of the box. Extensive
empirical results on both text and image classification tasks with diverse
datasets and backbone models (e.g., BERT and ResNet for text and image
classification) show the value of our proposed framework.
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