Context-Aware Feature Selection and Classification
Authors: Juanyan Wang, Mustafa Bilgic
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several datasets demonstrate that the proposed model outperforms eight baselines on a combined classification and feature selection measure, and is able to better emulate the ground-truth instancelevel feature selections. We conduct experiments to compare the proposed CFSC method to several baselines on both classification and feature selection performance. |
| Researcher Affiliation | Academia | Juanyan Wang and Mustafa Bilgic Illinois Institute of Technology, Chicago, IL, USA jwang245@hawk.iit.edu, mbilgic@iit.edu |
| Pseudocode | No | The paper describes its model architecture in text and with a diagram (Figure 1), but it does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The supplementary materials are available at https://github.com/IIT-ML/ IJCAI23-CFSC. |
| Open Datasets | Yes | The Credit [Goyal, 2020] dataset contains 3,254 bank credit card customers with 37 features and binary labels indicating if the customer is an Attrited Customer. The Company [Zieba et al., 2016] dataset has 4,182 companies with 64 features and binary labels indicating whether the company bankrupted within the forecasting period. The Mobile [Sharma, 2017] dataset contains 2,000 mobile phone data with 20 features and binary labels indicating if the price of a phone is in the high cost range. The NHIS [CDC, 2017] dataset has 2,306 adult survey data with 144 features and binary labels indicating if the person is suffering from chronic obstructive pulmonary disease. The Ride [City of Chicago, 2019] dataset has 4,800 ride trip records with 46 features and binary labels indicating if the trip is shared with other persons. |
| Dataset Splits | Yes | For each dataset, we use 1/3 of the data as the test set and perform 5-fold validation on the rest of the data where one fold is used for validation and four folds are used for training. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory specifications, or cloud instance types) used to run its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and activation functions such as 'sparsemax' and 'gumbel-softmax activation function', but it does not provide specific version numbers for any software dependencies, libraries, or frameworks used (e.g., Python version, TensorFlow/PyTorch versions, or specific library versions for baselines). |
| Experiment Setup | Yes | CFSC has one hidden layer with 16 units for the classification module and two hidden layers with 64 and 256 units respectively for the feature selection module. The ATT-FL model has one hidden layer with 64, one Bi LSTM layer with 32, and one attention layer with 256 units. The RNP model has one hidden layer with 16 units for the classification module and two hidden layers with 64 and 256 units respectively for the feature selection module. The FF model has one hidden layer with 16 units. We set γa to 0.5 (Equation 2) for all models. We performed grid search with cross validation to optimize all the other tunable hyper-parameters of each method using the combined measure on the validation set. |