pdf/uncertainty-and-interpretability-in-convolutional-neural-1xihs49trn.pdf

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps.

Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

/pdf/extreme-learning-machines-on-high-dimensional-and-large-data-1jmjrzvz2j.pdf

Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

Recently, many excellent algorithms for time series prediction issues have been proposed, most of which are developed based on the assumption that sufficient training data and testing data under the same distribution are available. However, in reality, time-series data usually exhibit some kind of time-varying characteristic, which may lead to a wide variability between old data and new data. Hence, how to transfer knowledge over a long time span, when addressing time series prediction issues, poses serious challenges. To solve this problem, in this paper, a hybrid algorithm based on transfer learning, Online Sequential Extreme Learning Machine with Kernels (OS-ELMK), and ensemble learning, abbreviated as TrEnOS-ELMK, is proposed, along with its precise mathematic derivation. It aims to make the most of, rather than discard, the adequate long-ago data, and constructs an algorithm framework for transfer learning in time series forecasting, which is groundbreaking. Inspired by the preferable performance of models ensemble, ensemble learning scheme is also incorporated into our proposed algorithm, where the weights of the constituent models are adaptively updated according to their performances on fresh samples. Compared to many existing time series prediction methods, the newly proposed algorithm takes long-ago data into consideration and can effectively leverage the latent knowledge implied in these data for current prediction. In addition, TrEnOS-ELMK naturally inherits merits of both OS-ELMK and ensemble learning due to its incorporation of the two techniques. Experimental results on three synthetic and six real-world datasets demonstrate the effectiveness of the proposed algorithm.

A novel transfer learning framework for time series forecasting

Apply Multiple Kernel Boosting and Multiclass KELM to Network Intrusion Detection.Tested approach on several machine learning datasets and the KDD Cup 99 dataset.Utilized Fractional Polynomial Kernels for the Network ID problem for the first time.Requires no feature selection, minimal pre-processing and works on imbalanced data.Achieves superior detection rates and lower false alarm rates than other approaches. Detection of cyber-based attacks on computer networks continues to be a relevant and challenging area of research. Daily reports of incidents appear in public media including major ex-filtrations of data for the purposes of stealing identities, credit card numbers, and intellectual property as well as to take control of network resources. Methods used by attackers constantly change in order to defeat techniques employed by information technology (IT) teams intended to discover or block intrusions. "Zero Day" attacks whose "signatures" are not yet in IT databases are continually being uncovered. Machine learning approaches have been widely used to increase the effectiveness of intrusion detection platforms. While some machine learning techniques are effective at detecting certain types of attacks, there are no known methods that can be applied universally and achieve consistent results for multiple attack types. The focus of our research is the development of a framework that combines the outputs of multiple learners in order to improve the efficacy of network intrusion on data that contains instances of multiple classes of attacks. We have chosen the Extreme Learning Machine (ELM) as the core learning algorithm due to recent research that suggests that ELMs are straightforward to implement, computationally efficient and have excellent learning performance characteristics on par with the Support Vector Machine (SVM), one of the most widely used and best performing machine learning platforms (Liu, Gao, & Li, 2012). We introduce the novel Multiple Adaptive Reduced Kernel Extreme Learning Machine (MARK-ELM) which combines Multiple Kernel Boosting (Xia & Hoi, 2013) with the Multiple Classification Reduced Kernel ELM (Deng, Zheng, & Zhang, 2013). We tested this approach on several machine learning datasets as well as the KDD Cup 99 (Hettich & Bay, 1999) intrusion detection dataset. Our results indicate that MARK-ELM works well for the majority of University of California, Irvine (UCI) Machine Learning Repository small datasets and is scalable for larger datasets. For UCI datasets we achieved performance similar to the MKBoost Support Vector Machine (SVM) approach. In our experiments we demonstrate that MARK-ELM achieves superior detection rates and much lower false alarm rates than other approaches on intrusion detection data.

Mark-elm

Liver cancer is one of the world’s largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.

/pdf/liver-cancer-detection-using-hybridized-fully-convolutional-2r00z836l1.pdf

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or nontumor class. A voxel is represented with a rich feature vector that distinguishes itself from voxels in different classes. A fast learning algorithm Extreme Learning Machine (ELM) is trained as a voxel classifier. In automatic liver tumor detection, we propose and show that ELM can be trained as a one-class classifier with only healthy liver samples in training. It results in a method of tumor detection based on novelty detection. We compare it with two-class ELM. To extract the boundary of a tumor, we adopt the semi-automatic approach by randomly selecting samples in 3D space within a limited region of interest (ROI) for classifier training. Our approach is validated on a group of patients' CT data and the experiment shows good detection and encouraging segmentation results.

Liver tumor detection and segmentation using kernel-based extreme learning machine

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Liver tumor detection and segmentation using kernel-based extreme learning machine