Name
Abstract | ||
|---|---|---|
Since traditional machine learning (ML) techniques use black-box model, the internal operation of the classifier is unknown to human. Due to this black-box nature of the ML classifier, the trustworthiness of their predictions is sometimes questionable. Interpretable machine learning (IML) is a way of dissecting the ML classifiers to overcome this shortcoming and provide a more reasoned explanation of model predictions. In this paper, we explore several IML methods and their applications in various domains. Moreover, a detailed survey of IML methods along with identifying the essential building blocks of a black-box model is presented here. Herein, we have identified and described the requirements of IML methods and for completeness, a taxonomy of IML methods which classifies each into distinct groupings or sub-categories, is proposed. The goal, therefore, is to describe the state-of-the-art for IML methods and explain those in more concrete and understandable ways by providing better basis of knowledge for those building blocks and our associated requirements analysis. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSCI47803.2020.9308404 | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | DocType | ISBN |
Interpretable machine learning,taxonomy,survey,black box machine learning,machine learning | Conference | 978-1-7281-2548-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saikat Das | 1 | 0 | 0.34 |
| Namita Agarwal | 2 | 0 | 0.34 |
| Deepak Venugopal | 3 | 8 | 3.15 |
| Frederick Sheldon | 4 | 86 | 16.46 |
| Sajjan G. Shiva | 5 | 116 | 23.02 |