Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Authors: Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments, we demonstrate the usefulness of the proposed methods. |
| Researcher Affiliation | Academia | 1The University of Tokyo, Japan 2RIKEN, Japan. |
| Pseudocode | No | The paper describes procedures in text, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper mentions LIBSVM software (http://www.csie.ntu.edu.tw/~cjlin/libsvm), which is a third-party tool, but does not provide access to the authors' own implementation code for the methodology described in this paper. |
| Open Datasets | Yes | We used sixteen benchmark data sets taken from the UCI Machine Learning Repository (Lichman, 2013), the Semi-Supervised Learning book (Chapelle et al., 2006), the LIBSVM (Chang & Lin, 2011), the ELENA Project,4 and a paper by Chapelle & Zien (2005).5 |
| Dataset Splits | Yes | We selected all hyper-parameters with validation samples of size 20 (n V P = n V N = 10). To compute the variance of the empirical PN and PNU risks, Var[ b RPN(bg PN)] and Var[ b Rη PNU(bg PN)], we repeatedly drew additional n V P = 10 positive, n V N = 10 negative, and n V U unlabeled samples from the rest of the data set. |
| Hardware Specification | Yes | All experiments were carried out using a PC equipped with two 2.60GHz Intel Xeon E5-2640 v3 CPUs. |
| Software Dependencies | No | The paper mentions software like “Caffe” and “LIBSVM” but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | As a classifier, we use the Gaussian kernel model: g(x) = Pn i=1 wi exp( x xi 2/(2σ2)), where n = n P + n N, {wi}n i=1 are the parameters, {xi}n i=1 = XP XN, and σ > 0 is the Gaussian bandwidth. The bandwidth candidates are {1/8, 1/4, 1/2, 1, 3/2, 2} median( xi xj n i,j=1). The classifier trained by minimizing the empirical PN risk is denoted by bg PN. The number of labeled samples for training is 20, where the class-prior was 0.5. In all experiments, we used the squared loss for training. |