Multi-Dimensional Classification via kNN Feature Augmentation
Authors: Bin-Bin Jia, Min-Ling Zhang3975-3982
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness of the proposed feature augmentation techniques, extensive experiments over eleven benchmark data sets as well as four state-of-the-art MDC approaches are conducted. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China 3Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 4Collaborative Innovation Center of Wireless Communications Technology, China jiabb@seu.edu.cn, zhangml@seu.edu.cn |
| Pseudocode | Yes | Table 1: The pseudo-code of KRAM. |
| Open Source Code | No | The paper provides a link to LIBSVM, a third-party library used in their experiments ('Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.'), but does not state that the code for their proposed KRAM approach is open-source or publicly available. |
| Open Datasets | Yes | The paper uses several benchmark data sets including those from the UCI machine learning repository (Dheeru and Karra Taniskidou 2017, http://archive.ics.uci.edu/ml) and other publicly available datasets referenced by citations (e.g., Karaliˇc and Bratko 1997, Dˇzeroski, Demˇsar, and Grbovi c 2000, Read, Bielza, and Larra naga 2014, Zhang and Zhou 2007, Boutell et al. 2004, Elisseeff and Weston 2002). |
| Dataset Splits | Yes | On each data set, ten-fold cross-validation is performed where the mean metric value as well as standard deviation are recorded for the comparing approaches. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Libsvm with linear kernel' and 'Na ıve Bayes (NB)' but does not specify their version numbers or any other software dependencies with specific versions. |
| Experiment Setup | Yes | A random cut of 67% examples from the original MDC training set is used to generate the base MDC model and the number of base classifiers is set to be 10 for ensemble approaches ECC, ECP and ESC. Furthermore, predictions of base MDC models are combined via majority voting. For KRAM, the only parameter k (number of nearest neighbors considered) is set to be 8. Support vector machine (SVM) (Chang and Lin 2011) and Na ıve Bayes (NB) are used as the multi-class classifier to implement each MDC approach, specifically Libsvm with linear kernel and NB with Gaussian pdf for continuous feature. |