More Like this

1. EDLT: Enabling Deep Learning for Generic Data Classification

   https://doi.org/10.1109/ICDM.2018.00030  

   Han, Huimei; Zhu, Xingquan; Li, Ying (November 2018, Proc. of the 18th IEEE International Conference on Data Mining (ICDM- 2018))

   This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix , we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification 

   more » « less
2. Learning Convolutional Neural Networks from Ordered Features of Generic Data

   https://doi.org/10.1109/ICMLA.2018.00145  

   Golinko, Eric; Sonderman, Thomas; Zhu, Xingquan (December 2018, The 17th IEEE International Conference on Machine Learning and Applications (ICMLA))

   Convolutional neural networks (CNN) have become very popular for computer vision, text, and sequence tasks. CNNs have the advantage of being able to learn local patterns through convolution filters. However, generic datasets do not have meaningful local data correlations, because their features are assumed to be independent of each other. In this paper, we propose an approach to reorder features of a generic dataset to create feature correlations for CNN to learn feature representation, and use learned features as inputs to help improve traditional machine learning classifiers. Our experiments on benchmark data exhibit increased performance and illustrate the benefits of using CNNs for generic datasets. 

   more » « less
3. Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations

   Dan Guo1, Melanie Christine (July 2020, Proceedings of Intelligent Systems for Molecular Biology (ISMB))

   Motivation: Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. Results: To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step towards accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. 

   more » « less
4. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery

   https://doi.org/10.3390/rs11070859  

   Buscombe, Daniel; Carini, Roxanne J. (April 2019, Remote Sensing)

   null (Ed.)

   We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification. 

   more » « less
5. Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

   https://doi.org/10.1109/MIPR49039.2020.00020  

   Tian, Haiman; Chen, Shu-Ching; Shyu, Mei-Ling (August 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR))

   null (Ed.)

   Abstract: Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks. 

   more » « less