Integrated Classification Model Research Articles

Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning

Common clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to the other methods, ARF-OOBEE has better evaluation indicator and is more effective for the early clinical diagnosis of rhinitis.

Neural integrated Markov model for effective script identification and classification in biometric system

Biometric scripts are required to be adaptable and robust for differentiating human input for preventing forging. However, based on the individual biometrics system's functionality, the script identification and classification are vital for input detection. Therefore, for improving the adaptability and improved analysis, this article introduces an Integrated Classification model (ICM) using Neural Markov Algorithm (NMA) for biometric systems. The proposed model jointly performs script detection and classification for different biometric functions. The flaws in the process are identified using output validation and the error between output and input. Therefore, this random change in error is computed using the Markov model for detection based on which the classification as robust or non-robust is decided. For improving the robust type classification, the current error level is accounted for and script flaws and improvements are recommended. Based on the script improvement further testing is performed for the detection phase using Markov stochastic computation. This detection is alone performed for preventing non-robust classifications for varying inputs. Therefore, the maximum robust combinations are identified across different biometric scripts that suit the design purpose.

RETRACTED ARTICLE: Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm

The transformation of innovation and entrepreneurship practice teaching and education methods has put forward higher requirements for the intelligence and personalization of online education platforms. The aim of this study is to predict learning outcomes based on students' learning outcomes and habits, identify weak areas of knowledge, and provide targeted guidance and recommend the most suitable teaching resources. According to the concept of LightGBM model and the method of Feature selection, the research puts forward an integrated classification model ELO–LightGBM based on Elo Rating System (ELO) scoring system and Light Gradient Boosting Machine (LightGBM), trying to further mine the potential information of the practical teaching management data set. The model obtained a score of 0.7928 when using the dataset training, and a large number of comparative experiments were carried out between the ELO–LightGBM model and other classification models in different public datasets. The experimental results proved that the ELO–LightGBM model is more accurate than other classification models. In the comparative experiment on the practical teaching data set, the accuracy of the ELO–LightGBM model also surpassed the LightGBM model and the linear support vector machine model that performed well in small data sets, and the model was in the accuracy rate. The accuracy rate of winners in the comparison of micro-average is as high as 82.6%. It can be seen that the ELO–LightGBM model is of great significance to the intelligence and personalization of the online education platform.

Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound

PurposeThe ultrasound (US) diagnosis of breast cancer is usually based on a single-region of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic performance. Multiple regions on multimodal US images of breast tumors may all have useful information for diagnosis. This study aimed to propose a multi-region radiomics approach with multimodal US for artificially intelligent diagnosis of malignant and benign breast tumors. Materials and methodsFirstly, radiomics features were extracted from five regions of interest (ROIs) on B-mode US and contrast-enhanced ultrasound (CEUS) images, including intensity statistics, gray-level co-occurrence matrix texture features and binary texture features. The multiple ROIs included the whole tumor region, strongest perfusion region, marginal region and surrounding region. Secondly, a deep neural network, composed of the point-wise gated Boltzmann machine and the restricted Boltzmann machine, was adopted to comprehensively learn and select features. Thirdly, the support vector machine was used for classification between benign and malignant breast tumors. Finally, five single-region classification models were generated from five ROIs, and they were fused to form an integrated classification model. ResultsExperimental evaluation was conducted on multimodal US images of breast from 187 patients with breast tumors (68 malignant and 119 benign). Under five-fold cross-validation, the classification accuracy, sensitivity, specificity, Youden's index and area under the receiver operating characteristic curve (AUC) with our model were 87.1% ± 3.3%, 77.4% ± 11.8%, 92.4% ± 7.2%, 69.8% ± 8.6% and 0.849 ± 0.043, respectively. Our model was significantly better than single-region single-modal methods in terms of the AUC and accuracy (p < 0.05). ConclusionIn addition to the whole tumor region, the other regions including the strongest perfusion region, marginal region and surrounding region on US images can assist breast cancer diagnosis. The multi-region multimodal radiomics model achieved the best classification results. Our artificially intelligent model would be potentially useful for clinical diagnosis of breast cancer.

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

In English education, British and American literature is a new type of course. The teaching of British and American literature has also undergone many reforms. In the practice of teaching reform, artificial intelligence- (AI-) assisted teaching such as machine learning (ML) has a long history. The performance is continuously improved by studying the mechanism of computer simulation of the human brain learning British and American literature. Then, computer intelligence can be realized. Based on this, this paper mainly discusses two aspects. One is the sentiment tendency analysis method based on the sentiment dictionary, and the other is the sentiment tendency analysis method based on ML. It mainly introduces the judgment of different emotional tendencies by the sentiment analysis model, which is an automatic review analysis and ensemble classification approach. The improvement of sentiment analysis improves the recognition range of text sentiment words in British and American literature teaching to optimize the process of text analysis. Its main feature is that the sentiment analysis of text directly acts on the tendency of words, with fine granularity and accurate analysis. Finally, it is concluded that the maximum value of the algorithm proposed here is 0.9, which has higher accuracy than the maximum value of 0.81 of other analysis models. The results indicate that the integrated classification model combining British and American literature teaching with the dimensions hidden Markov model has relatively reasonable text analysis and high sentiment classification accuracy. In terms of British and American literature teaching, using ML algorithms can effectively help teachers teach British and American literature through sentiment analysis.

A software defect prediction method based on sampling and integration

This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. SISDP). SISDP firstly constructs a robust KNN marking model by taking a balanced sample of samples to mark a batch of unmarked data, and then iteratively adds the newly marked data to the original data set for the next marking model. , iterate until the data is marked. For the marked data set, the hybrid sampling algorithm is used to obtain the training set, and the integrated classification model composed of the multi-classification algorithm is classified and trained. SISDP not only reduces the interference of a few classes on the marking process, but also improves the generalization ability of the defect prediction model.

Efficient image classification technique for weather degraded fruit images

Fruit image classification is an ill-posed problem. Many machine learning techniques have been developed until now to improve the classification problem of fruit images. However, the performance of these techniques depends upon the quality of acquired fruit images. Thus, the performance of competitive fruit classification techniques reduces for images captured under poor environmental conditions, such as haze, fog, smog etc. To overcome this issue, type-II fuzzy-based fruit image improvement approach is employed to improve the visibility of weather degraded fruit images. After that, fruit images will be classified using an integrated classification model. The integrated model combines two well-known models (i.e. CNN and RNN). CNN is utilised to evaluate the discriminative features of fruit images. RNN is utilised to asses sequential labels. Extensive analysis shows that the proposed integrated classification model outperforms competitive fruit image classification techniques in terms of accuracy and coefficient of correlation.

An integrated classification model for incremental learning.

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts.

Research on Electricity Information Acquisition System Based on Sample Data Mining Model

In power user information collection and detection, power companies generally have a variety of different detection needs, or need to solve the problem while having additional requirements for certain aspects. Therefore, the SVM classification technology is used in the paper to carry out more detailed pattern recognition of power consumption characteristics for small-scale users or users with major suspicions. Moreover, given the imbalance of the abnormal electricity detection data set, a comprehensive processing model of unbalanced samples is constructed. Meanwhile, the differential evolution algorithm is applied to complete the SVM parameter optimization, which not only solves the problem that the SVM classification performance is significantly affected by the parameters, but also ensures the operating efficiency of the integrated classification model.

Recognition of Multifunction Radars Via Hierarchically Mining and Exploiting Pulse Group Patterns

Recognition of multifunction radar (MFR) is an open problem in the field of electronic intelligence. Parameters of MFR pulses are generally agile and difficult to distinguish statistically. A prospective way to realize credible MFR recognition is mining and exploiting more distinguishable high-dimensional patterns buried in pulse groups, which may be designed for implementing infrequently used radar modes such as target tracking. A high-dimensional pattern is defined according to the agile range and switching law of sequential pulse repetitive intervals within a pulse group. This article establishes deep recurrent neural networks (RNN) to discriminate and coarsely cluster different pulse groups hierarchically with respect to their sequential structures. Afterwards, RNN-based classifiers are trained to extract and exploit features within different pulse group clusters. Distinct degrees of confidence are then attached to these classifiers to indicate the discriminabilities of the corresponding pulse group clusters. The pulse group clustering and classifying models are finally cascaded to form an integrated classification model, which mines distinguishable patterns from sequentially arriving pulse groups of the same radar and accumulate them to realize MFR recognition. Simulation results demonstrate the much improved performance of the proposed method over existing counterparts in different scenarios.

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

Aspect-based sentiment analysis using smart government review data

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Treatment-naïve first episode depression classification based on high-order brain functional network

BackgroundRecent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging. MethodsWe enrolled 82 treatment-naïve first episode depression (FED) adults and 72 matched normal control (NC). A computer-aided diagnosis framework was utilized to classify the FEDs from the NCs based on the features extracted from not only traditional “low-order” FC networks (LON) based on temporal synchronization of original rs-fMRI signals, but also “high-order” FC networks (HON) that characterize more complex functional interactions via correlation of the dynamic (time-varying) FCs. We contrasted a classifier using HON feature (CHON) and compared its performance with using LON only (CLON). Finally, an integrated classification model with both features was proposed to further enhance FED classification. ResultsThe CHON had significantly improved diagnostic accuracy compared to the CLON (82.47% vs. 67.53%). Joint classification further improved the performance (83.77%). The brain regions with potential diagnostic values mainly encompass the high-order cognitive function-related networks. Importantly, we found previously less-reported potential imaging biomarkers that involve the vermis and the crus II in the cerebellum. LimitationsWe only used one imaging modality and did not examine data from different subtypes of depression. ConclusionsDepression classification could be significantly improved by using HON features that better capture the higher-level brain functional interactions. The findings suggest the importance of higher-level cerebro-cerebellar interactions in the pathophysiology of MDD.

Motor imagery based on adaptive parameterless empirical wavelet transform and selective integrated classification

Improving recognition rate of motor imagery (MI)-related electroencephalography (EEG) is of great importance for numerous brain computer interface (BCI) applications. However, the performance of a typical BCI system greatly relies on the effectiveness of the extracted features from raw EEG signals and the ability of the classifier to correctly identify different MI patterns. Therefore, in this paper, a new recognition method based on adaptive parameterless empirical wavelet transform (APEWT) and selective integrated classification model is proposed to enhance the classification accuracy of MI-related EEG signal. First, the APEWT is used to decompose EEG signals from different MI patterns into several intrinsic mode functions (IMFs), each of which contains different rhythm information over different frequency bands. Then several related modes are optimally selected based on the correlation coefficients calculated between each IMF component and the original signal to reconstruct EEG signals. Next, in order to further extract useful pattern information from both the time domain and frequency domain, the energy spectrum features of multiple time segments from the reconstructed signals and marginal spectrum features of different frequency bands corresponding to those selected modes are investigated, respectively. Finally, the extracted multiple features from time domain and frequency domain are input into the selective integrated classification model to build an MI recognition system. The selective integrated classification model consists of several extreme learning machines (ELMs) as the basic classifiers, different weights are assigned, respectively, to ELM basic classifiers based on their corresponding classification performances, and several basic ELM classifiers with good performances are selected to construct the final integrated model. The proposed method is evaluated on two public datasets, including BCI Competition Ⅱ dataset Ⅲ and BCI Competition IV dataset 2 b, and is compared with four different combination methods where different features in time domain or frequency domain in the feature extraction stage and different ELMs based classification models are considered. Experimental results demonstrate that the proposed method outperformed four combination methods and the existing methods recently reported in the literature using the same datasets in terms of classification accuracy and area under the ROC curve receiver operating characteristic metric. Specifically, our proposed method achieves the highest average classification accuracy (89.95%) in the compared methods, which indicates its better classification performance and generalization capability. In addition, the proposed method exhibits high computational efficiency, thus providing a new solution for online recognition of MI-related BCI and having the potential to be embedded in a practical system for controlling an external device.

Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork

Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine.

BackgroundMicroRNAs (miRNAs) are a family of non-coding RNAs approximately 21 nucleotides in length that play pivotal roles at the post-transcriptional level in animals, plants and viruses. These molecules silence their target genes by degrading transcription or suppressing translation. Studies have shown that miRNAs are involved in biological responses to a variety of biotic and abiotic stresses. Identification of these molecules and their targets can aid the understanding of regulatory processes. Recently, prediction methods based on machine learning have been widely used for miRNA prediction. However, most of these methods were designed for mammalian miRNA prediction, and few are available for predicting miRNAs in the pre-miRNAs of specific plant species. Although the complete Solanum lycopersicum genome has been published, only 77 Solanum lycopersicum miRNAs have been identified, far less than the estimated number. Therefore, it is essential to develop a prediction method based on machine learning to identify new plant miRNAs.ResultsA novel classification model based on a support vector machine (SVM) was trained to identify real and pseudo plant pre-miRNAs together with their miRNAs. An initial set of 152 novel features related to sequential structures was used to train the model. By applying feature selection, we obtained the best subset of 47 features for use with the Back Support Vector Machine-Recursive Feature Elimination (B-SVM-RFE) method for the classification of plant pre-miRNAs. Using this method, 63 features were obtained for plant miRNA classification. We then developed an integrated classification model, miPlantPreMat, which comprises MiPlantPre and MiPlantMat, to identify plant pre-miRNAs and their miRNAs. This model achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum. Using miPlantPreMat, 522 Solanum lycopersicum miRNAs were identified in the Solanum lycopersicum genome sequence.ConclusionsWe developed an integrated classification model, miPlantPreMat, based on structure-sequence features and SVM. MiPlantPreMat was used to identify both plant pre-miRNAs and the corresponding mature miRNAs. An improved feature selection method was proposed, resulting in high classification accuracy, sensitivity and specificity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0423-x) contains supplementary material, which is available to authorized users.

The Temporally Integrated Monitoring of Ecosystems (TIME) Project Design: 1. Classification of Northeast Lakes Using a Combination of Geographic, Hydrogeochemical, and Multivariate Techniques

This investigation is part of the Temporally Integrated Monitoring of Ecosystems ((TIME) project, an effort to meet the difficult challenge of monitoring surface water quality in the northeastern United States for signs of change in response to the Clean Air Act Amendments of 1990. The overall objective of the study was to develop a unified scheme for classifying lakes in the northeast into relatively homogeneous groups and improve the likelihood of detecting water quality trends in the region. The study approach involved combining the best elements of several procedures recently used for defining regional subpopulations of lakes; these were termed the hydrogeochemical model (HM), geographical model (GM), and multivariate statistical model (MSM). Lake and watershed data from the U.S. Environmental Protection Agency Eastern Lake Survey (ELS) were used to evaluate the classification methods and their modifications. After preliminary comparisons were made of the three classification schemes, it was concluded that the resulting subpopulations indicated that there was meaningful similarity among methods but that the significant dissimilarity reflected distinctive attributes of each classification method. These differences were deemed important; accordingly, integration of the methods entailed efforts to preserve parts of each. This was accomplished by assigning each lake of the ELS data set into a lake cluster that had been defined by jointly applying the HM and GM methods. Subsequently, the jointly classified clusters were aggregated by coupling an application of the MSM (cluster analysis) with subjective judgment regarding termination of the process of cluster formation. This integration of procedures gave rise to nine subpopulations that separated mainly on the basis of hydrogeochemical factors, though geographic influences also were evident in the results. The integrated classification procedure provided an explicit method involving the combination of several kinds of data to yield lake subpopulations. Although the process of integrating this information may stand alone as a useful exercise, the results obtained from the integrated classification model exhibited less dispersion than those formed by the parent procedures. This is an important consideration when subpopulation homogeneity is wanted as a means to improve trend detection. From the view of statistical efficiency, therefore, the integrated procedure may be considered to be more optimal than the parent schemes. When the complexity of the integrated approach is considered, however, we conclude that the next most precise classification scheme, the HM approach, may be preferred over the integrated classification for use in the design of an actual monitoring program.