Transductive Optimization of Top
Authors: k
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and analysis confirm the benefit of incoporating k in the learning process. In our experimental evaluations, the performance of TTK matches or exceeds existing state-of-the-art methods on 7 benchmark datasets for binary classification and 3 reserve design problem instances. |
| Researcher Affiliation | Collaboration | Li-Ping Liu,1 Thomas G. Dietterich,1 Nan Li, 2 Zhi-Hua Zhou2 1EECS, Oregon State University, Corvallis, Oregon 97331, USA 2National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {liuli@eecs.oregonstate.edu, tgd@oregonstate.edu}, {lin, zhouzh}@lamda.nju.edu.cn Nan Li is now working at Alibaba Group, Hangzhou China. |
| Pseudocode | Yes | Algorithm 1 Find a descending feasible direction |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the methodology described. |
| Open Datasets | Yes | Seven datasets, {diabetes, ionosphere, sonar, spambase, splice} from UCI repository and {german-numer, svmguide3} from the LIBSVM web site, are widely studied binary classification datasets. The other three datasets, NY16, NY18 and NY88, are three species distribution datasets extracted from a large e Bird dataset [Sullivan et al., 2009] |
| Dataset Splits | Yes | Each algorithm is run 10 times on 10 random splits of each dataset. Each of these algorithms requires setting the regularization parameter C. This was done by performing five 2-fold internal cross-validation runs within each training set and selecting the value of C from the set {0.01, 0.1, 1, 10, 100} that maximized precision on the top 5% of the (cross-validation) test points. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Gurobi and Univer SVM implementation but does not provide specific version numbers for these or other software dependencies. For example, it states: "We used Gurobi[Gurobi Optimization, 2015]." without specifying a version number for Gurobi itself. |
| Experiment Setup | Yes | We set k to select 5% of the test instances. For the SVM and AATP methods, we fit them to the training data and then obtain a top-k prediction by adjusting the intercept term b. The hyper-parameter C is set to 1 for all methods. |