Multi-Source Iterative Adaptation for Cross-Domain Classification
Authors: Himanshu S. Bhatt, Arun Rajkumar, Shourya Roy
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results justify the theory as MSIDA significantly outperforms existing cross-domain classification approaches on the real world and benchmark datasets. 6 Experiments The efficacy of the proposed algorithm is evaluated for crossdomain sentiment classification task and the performance is reported in terms of classification accuracy. |
| Researcher Affiliation | Industry | Himanshu S. Bhatt, Arun Rajkumar and Shourya Roy Xerox Research Centre India, Bengaluru, INDIA {Firstname.Lastname}@xerox.com |
| Pseudocode | Yes | Algorithm 1 Greedy Algorithm for Selecting Sources. Algorithm 2 Multi-source Iterative Learning Algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The first dataset comprises the widely used Amazon review dataset [Blitzer et al., 2007] appended with the Amazon product dataset [Mc Auley et al., 2015b; 2015a] for evaluating the challenges of multi-source adaptation. |
| Dataset Splits | No | The paper specifies using '1600 reviews from each source-target domain pair are used to learn shared representations' and 'For target domains, 1600 reviews serve as unlabeled pool of instances' (training data), and 'performance is reported on the non-overlapping 400 reviews' (test data). However, a distinct validation split is not mentioned. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'SVM classifiers with radial basis function (RBF) kernels' but does not specify the software library or version number used for the SVM implementation (e.g., scikit-learn, LIBSVM). |
| Experiment Setup | Yes | we used SVM classifiers with radial basis function (RBF) kernels as individual classifiers combined with uniformly initialized weights in the ensemble and the maximum number of iterations (iter Max) set to 30. 1 and 2 are set empirically on a held-out set |