Query: ensemble learning broad learning system

title: Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning
abstract: An aircraft engine (aeroengine) operates in an extremely harsh environment, causing the working state of the engine to constantly change. As a result, the engine is prone to various kinds of wear faults. This paper proposes a new intelligent method for the diagnosis of aeroengine wear faults based on oil analysis, in which broad learning system (BLS) and ensemble learning models are introduced and integrated into the bagging BLS model, in which 100 sub BLS models are established, which are further optimized by ensemble learning. Experiments are conducted to verify the proposed method, based on the analysis of oil data, in which the random forest and single BLS algorithms are used for comparison. The results show that the output accuracy of the proposed method is stable (at 0.988) showing that the bagging BLS model can improve the accuracy and reliability of engine wear fault diagnosis, reflecting the development trend of fault diagnosis in implementing intelligent technology.

title: LPI BLS: Predicting lncRNA protein interactions with a broad learning system based stacked ensemble classifier
abstract: Many experiment results show that long non coding RNAs (lncRNAs) play crucial roles in many biological processes, implementing their functions through interaction with RNA binding proteins (RBPs) Considering that the experimental identification of lncRNA protein interactions is expensive and time consuming, many computational methods are proposed to uncover the potential lncRNA protein interactions. In this study, we develop a novel computational method (namely LPI BLS) to predict the lncRNA protein interactions by using the broad learning system and building a stacked ensemble classifier with a logistical regression model. LPI BLS first adopts the broad learning system to predict the lncRNA protein interactions. Broad learning system is an alternative way of learning in deep structure and a flat network with few parameters. Then, the results of multiple individual broad learning systems are fed into the stacked ensemble classifier built with a logistical regression to further improve the predictive performance. Compared with other state of the art methods in 5 fold cross validation test, LPI BLS has the best performance with the accuracy of 0.902 on RPI488 dataset, the average accuracy of 0.927 on RPI7317 dataset. The results in the independent test also show that our LPI BLS can effectively predict the lncRNA protein interactions. The source code can be freely downloaded from https:/github.com/NWPU 903PR/LPI_BLS

title: A Broad Learning System with Ensemble and Classification Methods for Multi step ahead Wind Speed Prediction
abstract: Short term wind speed prediction plays a significant role in the management of large scale wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and non linearity of wind. For this purpose, a broad learning system (BLS) with ensemble and classification named BLS EC is proposed to predict multi step ahead wind speed. The proposed method is based on a new neural network termed the BLS, which could work out the complex non linear relation by learning model while ensuring the computational efficiency. To overcome the randomness and instability of a single BLS, this paper proposes the BLS ensemble method to improve the generalization and stability of the network. In order to improve the accuracy of prediction, a method called classification guided regression is proposed to distinguish different variation patterns of initial predicted wind speed. According to the classification result, different pattern sequences are re predicted to obtain the final prediction result. Applying this thinking and method into research of three real time wind speed datasets which were taken from Sotavento Galicia SA (SG) Alberta (ALB) and Newfoundland (NFL) the validity and practical value of this method can be demonstrated. Results obtained clearly show that BLS is better than existing methods ARIMA and RBF. Moreover, the BLS EC method improved generalization performance and the predicting precision of a single BLS. In this study, the BLS EC was proposed and successfully applied to wind speed prediction.

title: Recognition of CRISPR/Cas9 off target sites through ensemble learning of uneven mismatch distributions
abstract: Cas9 is driving a broad range of innovative applications from basic biology to biotechnology and medicine. One of its current issues is the effect of off target editing that should be critically resolved and should be completely avoided in the ideal use of this system.  We developed an ensemble learning method to detect the off target sites of a single guide RNA (sgRNA) from its thousands of genome wide candidates. Nucleotide mismatches between on target and off target sites have been studied recently. We confirm that there exists strong mismatch enrichment and preferences at the 5' end close regions of the off target sequences. Comparing with the on target sites, sequences of no editing sites can be also characterized by GC composition changes and position specific mismatch binary features. Under this novel space of features, an ensemble strategy was applied to train a prediction model. The model achieved a mean score 0.99 of Aera Under Receiver Operating Characteristic curve and a mean score 0.45 of Aera Under Precision Recall curve in cross validations on big datasets, outperforming state of the art methods in various test scenarios. Our predicted off target sites also correspond very well to those detected by high throughput sequencing techniques. Especially, two case studies for selecting sgRNAs to cure hearing loss and retinal degeneration partly prove the effectiveness of our method. Availability and implementation The python and matlab version of source codes for detecting off target sites of a given sgRNA and the supplementary files are freely available on the web at https:/github.com/penn hui/OfftargetPredict. Supplementary information Supplementary data are available at Bioinformatics online.

title: EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets
abstract: This paper describes our system submitted to IEST 2018, a shared task (Klinger et al. 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three different approaches: feed forward neural network (FFNN) convolutional BLSTM (ConBLSTM) and Gradient Boosting Regression Tree Method (GBM) Word embeddings used in convolutional BLSTM are pre trained on 470 million tweets which are filtered using the emotional words and emojis. In addition, broad sets of features (i.e. syntactic features, lexicon features, cluster features) are adopted to train GBM and FFNN. The three approaches are finally ensembled by the weighted average of predicted probabilities of each emotion label.

title: Face pose estimation with ensemble multi scale representations
abstract: Face pose estimation plays important roles in broad applications such as visual based surveillance, face authentication, human computer intelligent interactions, etc. However, face pose estimation is also a challenge issue, especially under complicated real application environments. In this paper, we proposed a novel face pose estimation approach with integrating two multi scale representations. The first one is multi scale VGG Face representations, which using VGG Face CNN as backbone three middle scale layer outputs are extracted and go through additional transfer learning. The second one is multi scale Curvelet representations. These two sub multi scale representations are integrated and then several dense layers processing are added to form the entire ensemble system which is used for the prediction of face pose. The experiment results show that the proposed approach achieved mean absolute errors (MAE) of 0.33deg and 0.23deg for yaw and pitch angle on CAS PEAL pose database, and achieved mean absolute errors of 3.88deg and 1.98deg for yaw and pitch angle on Pointing'04 database.

title: BNGBS: An efficient network boosting system with triple incremental learning capabilities for more nodes, samples, and classes
abstract: As an ensemble algorithm, network boosting enjoys a powerful classification ability but suffers from the tedious and time consuming training process. To tackle the problem, in this paper, a broad network gradient boosting system (BNGBS) is developed by integrating gradient boosting machine with broad networks, in which the classification loss caused by a base broad network is learned and eliminated by followed networks in a cascade manner. The proposed system is constructed as an additive model and can be easily optimized by a greedy strategy instead of the tedious back propagation algorithm, resulting in a more efficient learning process. Meanwhile, triple incremental learning capabilities including the increment of feature nodes, increment of input samples, and increment of target classes are designed. The proposed system can be efficiently updated and expanded based on the current status instead of being entirely retrained when the demands for more feature nodes, input samples, and target classes are proposed. The node increment ability allows to add more feature nodes into the built system if the current structures are not effective for learning. The sample increment ability is developed to allow the model to keep learning from the coming batch data. The class increment ability is used to tackle the issue that the coming batch data may contain unseen categories. In comparison with existing popular machine learning methods, comprehensive results based on eight benchmark datasets illustrate the effectiveness of the proposed broad network gradient boosting system for the classification task.

title: Music Genre Recognition Using Deep Neural Networks and Transfer Learning
abstract: Music genre recognition is a very interesting area of research in the broad scope of music information retrieval and audio signal processing. In this work we propose a novel approach for music genre recognition using an ensemble of convolutional long short term memory based neural networks (CNN LSTM) and a transfer learning model. The neural network models are trained on a diverse set of spectral and rhythmic features whereas the transfer learning model was originally trained on the task of music tagging. We compare our system with a number of recently published works and show that our model outperforms them and achieves new state of the art results.

title: MUSICAL GENRE AND STYLE RECOGNITION USING DEEP NEURAL NETWORKS AND TRANSFER LEARNING
abstract: Music genre and style recognition are very interesting areas of research in the broad scope of music information retrieval and audio signal processing. In this work we propose a novel approach for music genre and style recognition using an ensemble of convolutional neural network (CNN) convolutional long short term memory network (CNN LSTM) and a transfer learning model. The neural network models are trained on a diverse set of spectral and rhythmic features whereas the transfer learning model was originally trained on the task of music tagging. We compare our system with a number of recently published works and show that our model outperforms them and achieves new state of the art results.

title: System Fusion with Deep Ensembles
abstract: Deep neural networks (DNNs) are universal estimators that have achieved state of the art performance in a broad spectrum of classification tasks, opening new perspectives for many applications. One of them is addressing ensemble learning. In this paper, we introduce a set of deep learning techniques for ensemble learning with dense, attention, and convolutional neural network layers. Our approach automatically discovers patterns and correlations between the decisions of individual classifiers, therefore, alleviating the difficulty of building such architectures. To assess its robustness, we evaluate our approach on two complex data sets that target different perspectives of predicting the user perception of multimedia data, i.e. interestingness and violence. The proposed approach outperforms the existing state of the art algorithms by a large margin.