How to Perform #FeatureSelection with Categorical Data
After completing this tutorial, you will know:
* The breast cancer predictive modeling problem with categorical inputs and binary #classification target variable.
* How to evaluate the importance of categorical features using the chi-squared and mutual information statistics.
* How to perform feature selection for categorical data when fitting and evaluating a classification model.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
* The breast cancer predictive modeling problem with categorical inputs and binary #classification target variable.
* How to evaluate the importance of categorical features using the chi-squared and mutual information statistics.
* How to perform feature selection for categorical data when fitting and evaluating a classification model.
Link
🔭 @DeepGravity
#Classification-driven Single Image Dehazing
Most existing dehazing algorithms often use hand-crafted features or #ConvolutionalNeuralNetworks (#CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper, we investigate a new point of view in addressing this problem. Instead of focusing only on achieving good quantitative performance on pixel-based metrics such as Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself does not degrade the performance of the high-level vision tasks such as image classification. To this end, we present an unified CNN architecture that includes three parts: a dehazing sub-network (DNet), a classification-driven Conditional #GenerativeAdversarialNetworks sub-network (CCGAN) and a classification sub-network (CNet) related to image classification, which has better performance both on visual appeal and image classification. We conduct comprehensive experiments on two challenging benchmark datasets for fine-grained and object classification: CUB-200-2011 and Caltech-256. Experimental results demonstrate that the proposed method outperforms many recent state-of-the-art single image dehazing methods in terms of image dehazing metrics and classification accuracy.
Link
🔭 @DeepGravity
Most existing dehazing algorithms often use hand-crafted features or #ConvolutionalNeuralNetworks (#CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper, we investigate a new point of view in addressing this problem. Instead of focusing only on achieving good quantitative performance on pixel-based metrics such as Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself does not degrade the performance of the high-level vision tasks such as image classification. To this end, we present an unified CNN architecture that includes three parts: a dehazing sub-network (DNet), a classification-driven Conditional #GenerativeAdversarialNetworks sub-network (CCGAN) and a classification sub-network (CNet) related to image classification, which has better performance both on visual appeal and image classification. We conduct comprehensive experiments on two challenging benchmark datasets for fine-grained and object classification: CUB-200-2011 and Caltech-256. Experimental results demonstrate that the proposed method outperforms many recent state-of-the-art single image dehazing methods in terms of image dehazing metrics and classification accuracy.
Link
🔭 @DeepGravity
#Transform-Invariant #ConvolutionalNeuralNetworks for Image #Classification and Search
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
GitHub
jasonustc/caffe-multigpu
linux && windows compatible caffe. Contribute to jasonustc/caffe-multigpu development by creating an account on GitHub.
A Gentle Introduction to Imbalanced #Classification
After completing this tutorial, you will know:
* Imbalanced classification is the problem of classification when there is an unequal distribution of classes in the training dataset.
* The imbalance in the class distribution may vary, but a severe imbalance is more challenging to model and may require specialized techniques.
* Many real-world classification problems have an imbalanced class distribution, such as fraud detection, spam detection, and churn prediction.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
* Imbalanced classification is the problem of classification when there is an unequal distribution of classes in the training dataset.
* The imbalance in the class distribution may vary, but a severe imbalance is more challenging to model and may require specialized techniques.
* Many real-world classification problems have an imbalanced class distribution, such as fraud detection, spam detection, and churn prediction.
Link
🔭 @DeepGravity
Machine Learning Mastery
A Gentle Introduction to Imbalanced Classification
Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The…