Learning Using Unselected Features (LUFe)
Authors: Joseph G. Taylor, Viktoriia Sharmanska, Kristian Kersting, David Weir, Novi Quadrianto
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide exhaustive experimental evaluation on 49 textual datasets; the results demonstrate that LUFe can indeed improve classification performance compared to traditional combinatorial feature selection, without incurring extra costs at test time. |
| Researcher Affiliation | Academia | Joseph G. Taylor,1 Viktoriia Sharmanska1 Kristian Kersting,2 David Weir,1 and Novi Quadrianto1 1SMi Le CLi Ni C and TAG Lab, University of Sussex, Brighton, UK 2Technische Universit at Dortmund, Dortmund, Germany |
| Pseudocode | Yes | For the pseudocode of LUFe, please refer to algorithm 1. |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the described methodology was found. |
| Open Datasets | Yes | Dataset We follow the protocol of [Paul et al., 2015], in using a subset of the Tech TC-300 collection consisting of 49 datasets, pre-processed to remove all features corresponding to any word of less than 5 characters. The Tech TC-300 collection consists of 300 textual datasets, which have baseline SVM error rate uniformly distributed between 0.4 and 0.0.1 1http://techtc.cs.technion.ac.il/techtc300/techtc300.html |
| Dataset Splits | Yes | All experimental settings were tested over 100 repeats, and each repeat, stratified 5-fold cross-validation was used to estimate the λ parameters for each setting. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using SVM and a quadratic programming (QP) solver, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Model selection A consistent model selection procedure was carried out in all experiments. All experimental settings were tested over 100 repeats, and each repeat, stratified 5-fold cross-validation was used to estimate the λ parameters for each setting. All parameters were selected from seven log-spaced values in the range {10 3...103}). The two SVM+ parameters for LUFe (λ1 and λ2) were jointly optimised through grid search; that is, 49 combinations were assessed. |