Deep Neural Networks Constrained by Decision Rules
Authors: Yuzuru Okajima, Kunihiko Sadamasa2496-2505
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions. |
| Researcher Affiliation | Industry | Yuzuru Okajima, Kunihiko Sadamasa NEC Corporation 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa 211-8666, Japan y-okajima@bu.jp.nec.com, k-sadamasa@az.jp.nec.com |
| Pseudocode | Yes | Algorithm 1 Generalized EM algorithm |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | For time-series classification, we used the top five largest binary classification data from the UCR time series repository (Chen et al. 2015). For sentiment classification, three review datasets were used: IMDB for movies, Elec for electronics products (Maas et al. 2011), and Yelp for local businesses2. |
| Dataset Splits | Yes | The hyperparameters of CART, RF and SVMs were selected from Table 2 by grid search with five-fold cross validation using training data. The hyperparameter λ of SBRL was also selected from 100.5, 10, 101.5, 102, 102.5, and 103 by validation. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance specifications). |
| Software Dependencies | No | The paper mentions software like "scikit-learn implementations" and "R implementation of SBRL" but does not provide specific version numbers for these or any other ancillary software components, preventing reproducibility of the exact software environment. |
| Experiment Setup | Yes | The number of decision trees in RF was set to 100. The hyperparameters of CART, RF and SVMs were selected from Table 2 by grid search with five-fold cross validation using training data. The hyperparameter λ of SBRL was also selected from 100.5, 10, 101.5, 102, 102.5, and 103 by validation. The hidden layer size, h, was 128 for CNN and 512 for Bi-LSTM. The RCNs were first trained without rule set optimization, i.e., using all rules in R for time-series and sentiment classification. After that, they were trained with rule set optimization with sample size s set to 100. |