Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Model Metric Co-Learning for Time Series Classification
Authors: Huanhuan Chen, Fengzhen Tang, Peter Tino, Anthony G. Cohn, Xin Yao
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and benchmark data sets con firm the effectiveness of the algorithm compared to a variety of alternative methods. |
| Researcher Affiliation | Academia | Huanhuan Chen School of Computer Science, Univ. of Sci. & Tech. of China Hefei, Anhui, China EMAIL Fengzhen Tang, Peter Tino School of Computer Science University of Birmingham Birmingham, B15 2TT, UK fxt126,EMAIL Anthony G Cohn School of Computing University of Leeds Leeds, LS2 9JT, UK EMAIL Xin Yao School of Computer Science University of Birmingham Birmingham, B15 2TT, UK EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and prose but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper mentions external code for comparison methods but does not provide concrete access to the source code for the MMCL methodology described. |
| Open Datasets | Yes | We used 7 data sets from the UCR Time Series Repository [Keogh et al., 2011]. |
| Dataset Splits | Yes | All (hyper) parameters, such as the MMCL trade-off parameter λ, order p in the AR kernel, number of hidden states in the HMM based Fisher kernel, regularization parameter η for ridge regression etc. have been set by 5-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions LIBSVM but does not provide specific version numbers for software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | In MMCL, the number of nodes was fixed to N = 50 and 10 jumps (making the jump length 5). All (hyper) parameters, such as the MMCL trade-off parameter λ, order p in the AR kernel, number of hidden states in the HMM based Fisher kernel, regularization parameter η for ridge regression etc. have been set by 5-fold cross-validation on the training set. The SVM parameters, kernel width γ in eq. (13) and C, were tuned in the following ranges: γ {10 6, 10 5, , 101}, C {10 3, 10 2, , 103}. We also tested our MMCL method using a k-NN classi fier where k {1, 2, , 10}. |