Prayatul Matrix: A Direct Comparison Approach to Evaluate Performance of Supervised Machine Learning Models

@article{Biswas2022PrayatulMA,
  title={Prayatul Matrix: A Direct Comparison Approach to Evaluate Performance of Supervised Machine Learning Models},
  author={Anupam Biswas},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.12728}
}
—Performance comparison of supervised machine learning (ML) models are widely done in terms of different confusion matrix based scores obtained on test datasets. However, a dataset comprises several instances having different difficulty levels. Therefore, it is more logical to compare effectiveness of ML models on individual instances instead of comparing scores obtained for the entire dataset. In this paper, an alternative approach is proposed for direct comparison of supervised ML models in… 

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References

SHOWING 1-10 OF 30 REFERENCES

Multi-class imbalanced image classification using conditioned GANs

A model that uses a conditioned deep convolutional GAN and an auxiliary classifier are proposed to tackle the issue of data skewness, class imbalance, data scarcity and noise and has shown significant improvements over the other often used models of data augmentation and multi-class imbalance image classification.

Learning Multiple Layers of Features from Tiny Images

It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, using a novel parallelization algorithm to distribute the work among multiple machines connected on a network.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet.

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task and measures the power (ability to detect algorithm differences when they do exist) of these tests.

EMNIST: Extending MNIST to handwritten letters

A variant of the full NIST dataset is introduced, which is called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset, and one that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems.

Xception: Deep Learning with Depthwise Separable Convolutions

  • François Chollet
  • Computer Science
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.

A systematic analysis of performance measures for classification tasks