Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance
Authors: Atsutoshi Kumagai, Tomoharu Iwata
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method was demonstrated through experiments using synthetic and real-world data sets. We conducted experiments using two synthetic and four real-world data sets to confirm the effectiveness of the proposed method. |
| Researcher Affiliation | Industry | Atsutoshi Kumagai NTT Secure Platform Laboratories, NTT Corporation 3-9-11, Midori-cho, Musashino-shi, Tokyo, Japan kumagai.atsutoshi@lab.ntt.co.jp Tomoharu Iwata NTT Communication Science Laboratories, NTT Corporation 2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan iwata.tomoharu@lab.ntt.co.jp |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not include any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | We used four real-world data sets: SPAM21, ELEC22, ONP3, and BLOG 4. 1http://www.comp.dit.ie/sjdelany/Dataset.htm 2http://www.inescporto.pt/ jgama/ales/ales 5.html 3https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity 4https://archive.ics.uci.edu/ml/datasets/Blog Feedback |
| Dataset Splits | No | The paper specifies how training and testing data were split (e.g., 'remaining ten time units as test data', '80% of samples randomly at every training time unit to create ten different training data'), but it does not explicitly mention a separate validation set or split for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | For the proposed method and AAAI16, the number of iterations for learning was 2000 in all experiments. For Batch, Online and Present, we chose the regularization parameter from {10 1, 1, 101} in terms of which average AUC over all test time units was the best. |