A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios
Authors: Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
IJCAI 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several benchmark datasets clearly indicate the usefulness of the proposed approach over the existing state-of-the-art techniques. |
| Researcher Affiliation | Industry | Sri Harsha Dumpala, Rupayan Chakraborty and Sunil Kumar Kopparapu TCS Reseach and Innovation Mumbai, India EMAIL |
| Pseudocode | No | The paper describes its methods in narrative text and figures, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Proposed s2s L along with MLP and CSMLP techniques are implemented using Keras deep learning toolkit [KER, 2016]. There is no statement about the authors releasing their own code. |
| Open Datasets | Yes | All datasets used in this work are obtained from KEEL dataset repository [Fernandez et al., 2008]. |
| Dataset Splits | Yes | For each dataset, we use 5-fold (the folds as provided in the KEEL dataset repository are directly used) cross-validation approach to compare the performance of all the methods considered for analysis. ... Hence, at any time 80% of the data is used for training (75% as train set and 5% as validation set) and remaining 20% of the data is used for testing. The validation set is used for selecting network architecture and for hyper-parameter tuning. |
| Hardware Specification | Yes | Further, the average training time (in seconds) for convergence (using i5-3210M 3.1GHz cpu with 4-GB RAM) on Yeast6 for different techniques are: 98.7 (s2s L), 38.5 (MLP), 43.7 (CS-MLP), 213.8 (CSM), 95.3 (GSVM), 146.4 (EUSB). |
| Software Dependencies | No | Proposed s2s L along with MLP and CSMLP techniques are implemented using Keras deep learning toolkit [KER, 2016]. This mentions Keras but does not provide a specific version number for it. |
| Experiment Setup | Yes | For training s2s-MLP, we use Adam algorithm with an initial learning rate of 0.001. Binary cross-entropy is used as the cost function. The batch size and other hyper-parameters are selected considering the performance on the validation set. The number of units in the hidden layer is selected empirically by varying the hidden units from 2 to 4 d (twice the length of the input layer) |