Cost-sensitive deep neural network ensemble for class imbalance problem

In data mining, classification is a task to build a model which classifies data into a given set of categories. Most classification algorithms assume the class distribution of data to be roughly balanced. In real-life applications such as direct marketing, fraud detection and churn prediction, class imbalance problem usually occurs. Class imbalance problem is referred to the issue that the number of examples belonging to a class is significantly greater than those of the others. When training a standard classifier with class imbalance data, the classifier is usually biased toward majority class. However, minority class is the class of interest and more significant than the majority class. In the literature, existing methods such as data-level, algorithmic-level and cost-sensitive learning have been proposed to address this problem. The experiments discussed in these studies were usually conducted on relatively small data sets or even on artificial data. The performance of the methods on modern real-life data sets, which are more complicated, is unclear. In this research, we study the background and some of the state-of-the-art approaches which handle class imbalance problem. We also propose two costsensitive methods to address class imbalance problem, namely Cost-Sensitive Deep Neural Network (CSDNN) and Cost-Sensitive Deep Neural Network Ensemble (CSDE). CSDNN is a deep neural network based on Stacked Denoising Autoencoders (SDAE). We propose CSDNN by incorporating cost information of majority and minority class into the cost function of SDAE to make it costsensitive. Another proposed method, CSDE, is an ensemble learning version of CSDNN which is proposed to improve the generalization performance on class imbalance problem. In the first step, a deep neural network based on SDAE is created for layer-wise feature extraction. Next, we perform Bagging’s resampling procedure with undersampling to split training data into a number of bootstrap samples. In the third step, we apply a layer-wise feature extraction method to extract new feature samples from each of the hidden layer(s) of the SDAE. Lastly, the ensemble learning is performed by using each of the new feature samples to train a CSDNN classifier with random cost vector. Experiments are conducted to compare the proposed methods with the existing methods. We examine their performance on real-life data sets in business domains. The results show that the proposed methods obtain promising results in handling class imbalance problem and also outperform all the other compared methods. There are three major contributions to this work. First, we proposed CSDNN method in which misclassification costs are considered in training process. Second, we incorporate random undersampling with layer-wise feature extraction to perform ensemble learning. Third, this is the first work that conducts experiments on class imbalance problem using large real-life data sets in different business domains ranging from direct marketing, churn prediction, credit scoring, fraud detection to fake review detection

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Application of Convolutional Neural Network Framework on Generalized Spatial Modulation for Next Generation Wireless Networks

A novel custom auto-encoder Complex Valued Convolutional Neural Network (AE-CVCNN) model is proposed and implemented using MATLAB for multiple-input-multiple output (MIMO) wireless networks. The proposed model is applied on two dierent generalized spatial modulation (GSM) schemes: the single symbol generalized spatial modulation SS - GSM and the multiple symbol generalized spatial modulation (MS-GSM). GSM schemes are used with Massive-MIMO to increase both the spectrum eciency and the energy eciency. On the other hand, GSM schemes are subjected to high computational complexity at the receiver to detect the transmitted information. High computational complexity slows down the throughput and increases the power consumption at the user terminals. Consequently, reducing both the total spectrum eciency and energy eciency. The proposed CNN framework achieves constant complexity reduction of 22.73% for SSGSM schemes compared to the complexity of its traditional maximum likelihood detector (ML). Also, it gives a complexity reduction of 14.7% for the MS-GSM schemes compared to the complexity of its detector. The performance penalty of the two schemes is at most 0.5 dB. Besides to the proposed custom AE CV-CNN model, a dierent ML detector0s formula for SS -GSM schemes is proposed that achieves the same performance as the traditional ML detector with a complexity reduction of at least 40% compared to that of the traditional ML detector. In addition, the proposed AE-CV-CNN model is applied to the proposed ML detector,and it gives a complexity reduction of at least 63.6% with a performance penalty of less than 0.5 dB. An interesting result about applying the proposed custom CNN model on the proposed ML detector is that the complexity is reduced as the spatial constellation size is increased which means that the total spectrum eciency is increased by increasing the spatial constellation size without increasing the computational complexity

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis