A Filter Based Feature Selection for Imbalanced Text Classification

Swarnalatha, K.; Guru, D. S.; Anami, Basavaraj S.; Kumar, Nitish

doi:10.1007/978-981-13-9187-3_18

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…Reuters21578 BR [91] 0.434 0.835 0.402 PCC [103] 0.442 0.834 0.398 seq2seq-RNN [104] 0.384 0.858 0.437 BERT [94] 0.261 0.905 0.640 LBA [105] 0.254 0.908 0.675 ML-KNN [106] 0.376 0.732 0.253 ML-ARAM [61] 0.363 0.626 0.163 ML-RNN [104] 0.332 0.858 0.457 LaMP [107] 0.291 0.889 0.560 MPVAE [108] 0.267 0.887 0.542 MrMP [109] 0.254 0.893 0.591 Class-wise cluster + FS [110] -0.800 0.725 CB-NTR [111] 0.247 0.907 0.633 SHO-LSTM [45] 0.240 0.910 0.690 SHO-CNN (Proposed) 0.237 0.908 0.665 Slashdot BR [91] 0.052 0.486 0.362 BR-Support [112] 0.055 0.516 0.357 PCC [103] 0.056 0.480 0.279 seq2seq-RNN [104] 0.058 0.528 0.270 set-RNN [112] 0.053 0.538 0.310 BERT [94] 0.037 0.583 0.380 LBA [105] 0.038 0.582 0.410 CC [92] 0.057 0.480 0.373 CNN [25] 0.049 0.512 0.412 CNN-RNN [95] 0.046 0.530 0.469 MAGNET [112] 0.039 0.568 0.475 SHO-LSTM [45] 0.038 0.650 0.492 SHO-CNN (Proposed) 0.037 0.632 0.453 NELA-GT-2019 BR [91] 0.033 0.870 0.792 BR-Support [112] 0.040 0.846 0.783 PCC [103] 0.009 0.922 0.913 LBA [105] 0.008 0.953 0.9420 CNN [25] 0.008 0.934 0.926 CNN-RNN [95] 0.008 0.935 0.944 SHO-LSTM [45] 0.007 0.972 0.970 SHO-CNN (Proposed) 0.006 0.963 0.956…”

Section: Resultsmentioning

confidence: 99%

SHO-CNN: A Metaheuristic Optimization of a Convolutional Neural Network for Multi-Label News Classification

Nadeem

Ahmed

et al. 2022

Electronics

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News media always pursue informing the public at large. It is impossible to overestimate the significance of understanding the semantics of news coverage. Traditionally, a news text is assigned to a single category; however, a piece of news may contain information from more than one domain. A multi-label text classification model for news is proposed in this paper. The proposed model is an automated expert system designed to optimize CNN’s classification of multi-label news items. The performance of a CNN is highly dependent on its hyperparameters, and manually tweaking their values is a cumbersome and inefficient task. A high-level metaheuristic optimization algorithm, spotted hyena optimizer (SHO), has higher advanced exploration and exploitation capabilities. SHO generates a collection of solutions as a group of hyperparameters to be optimized, and the process is repeated until the desired optimal solution is achieved. SHO is integrated to automate the tuning of the hyperparameters of a CNN, including learning rate, momentum, number of epochs, batch size, dropout, number of nodes, and activation function. Four publicly available news datasets are used to evaluate the proposed model. The tuned hyperparameters and higher convergence rate of the proposed model result in higher performance for multi-label news classification compared to a baseline CNN and other optimizations of CNNs. The resulting accuracies are 93.6%, 90.8%, 68.7%, and 95.4% for RCV1-v2, Reuters-21578, Slashdot, and NELA-GT-2019, respectively.

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