@article{2939,
  author = {Amit Sangroya, Anantaram C, Mrinal Rawat, Mouli Rastogi},
  title = {Using Formal Concept Analysis to Explain Black Box Deep Learning Classification Models },
  journal = {Journal of E - Technology},
  year = {2020},
  volume = {11},
  number = {1},
  doi = {https://doi.org/10.6025/jet/2020/11/1/23-31},
  url = {http://www.dline.info/jet/fulltext/v11n1/jetv11n1_3.pdf},
  abstract = {Recently many machine learning based AI systems have been designed as black boxes. These are the systems that hide the internal logic from the users. Lack of transparency in decision making limits their use in various real world applications. In this paper, we propose a framework that utilizes formal concept analysis to explain AI models. We use classification
analysis to study abnormalities in the data which is further used to explain the outcome of machine learning model. The ML method used to demonstrate the ideas is two class classification problem. We validate the proposed framework using a real world machine learning task: diabetes prediction. Our results show that using a formal concept analysis approach can result in better explanations. },
}