Learning Latest Classifiers without Additional Labeled Data
Authors: Atsutoshi Kumagai, Tomoharu Iwata
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method is demonstrated with experiments using synthetic and real-world data sets. |
| Researcher Affiliation | Industry | Atsutoshi Kumagai NTT Secure Platform Laboratories kumagai.atsutoshi@lab.ntt.co.jp Tomoharu Iwata NTT Communication Science Laboratories iwata.tomoharu@lab.ntt.co.jp |
| Pseudocode | No | The paper does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | SPAM is a collection of spam and legitimate email received by one from February 1st of 2003 to January 31th of 2004 [Gama et al., 2014]. URL is a public data set of malicious and normal urls collected over 120 days [Ma et al., 2009]. |
| Dataset Splits | Yes | In our experiments, we chose the optimal hyper parameters for these methods from the following variations by using validation data. For SPAM, roughly, samples collected in the n-th month were used for labeled data, samples in the n+1-th month for unlabeled data, and samples in the n + 2-th month for test data, where n is 2, 3, , 11. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions types of models used like 'logistic regression' but does not specify software names with version numbers for dependencies (e.g., Python version, library versions). |
| Experiment Setup | Yes | In our experiments, we chose the optimal hyper parameters for these methods from the following variations by using validation data: regularization parameter for classifiers c {10 1, 1, 101} in all methods, regularization parameter for importance ρ {10 1, 1, 101} in the proposed method and IWLR, regularization parameter for imputation r, b {10 1, 1, 101}, a {1}, and imputation parameter K {1, 3, 6, 9} in the proposed method and ILR. |