Rescale-Invariant SVM for Binary Classification
Authors: Mojtaba Montazery, Nic Wilson
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, the presented approach is evaluated with 18 benchmarks, which are derived from six real data sets. and The experiments make use of the UCI machine learning repository from which six real data sets are chosen... and the presence of Tables 1, 2, 3, 4 detailing experimental results. |
| Researcher Affiliation | Academia | Mojtaba Montazery and Nic Wilson Insight Centre for Data Analytics School of Computer Science and IT University College Cork, Ireland {mojtaba.montazery, nic.wilson}@insight-centre.org |
| Pseudocode | No | The paper describes computational methods but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper provides a link to a 'longer document' for proofs and a glossary, but does not mention or provide a link for open-source code for the described methodology. |
| Open Datasets | Yes | The experiments make use of the UCI machine learning repository from which six real data sets are chosen, namely Breast Cancer Wisconsin [Street et al., 1993], Pima Indians Diabetes, Blood Transfusion Service Center [Yeh et al., 2009], Indian Liver Patient, Fertility [Méndez et al., 2012], and Banknote Authentication. |
| Dataset Splits | No | The paper states: 'For constructing a benchmark, a random selector creates two disjoint sets from a data set, one for learning (i.e., X) and one for testing.' It does not explicitly mention a 'validation' set or specific split percentages or detailed cross-validation strategy. |
| Hardware Specification | Yes | making use of a computer facilitated by a Core i7 2.60 GHz processor and 8 GB RAM memory. |
| Software Dependencies | Yes | The approach discussed in Section 5 was implemented using the solver CPLEX 12.6.2. |
| Experiment Setup | No | The paper describes how benchmarks are constructed and data is prepared, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or other model-specific training configurations. |