A Study of Random Linear Oracle Ensembles

Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?

Introduction. The Society of Marxist Historians established in 1925 went down in the history of Soviet historiography as a militant organization that did much to combat “old school” historians, assert the monopoly position of the Marxist-Leninist methodology, and draw a party line in historical science. Methods and materials. The research is based on traditional methods of historiographical analysis. It uses materials from historical journals of the 1920s and 1930s and archival documents. Analysis. The first all-Union conference of Marxist Historians (December 28, 1928 – January 4, 1929) became the apogee in the history of the Society. In the future, despite the growth in numbers and the creation of local structures, in the conditions of the “great turning point” it loses the features of an amateur organization and a number of functions of the scientific nature. The priority is to “actively participate in the socialist construction” by deploying mass propaganda of historical knowledge and fighting “distortions of Marxism-Leninism”, including in the ranks of the organization itself. The last debate and “study” of Stalin’s famous letter to “Proletarian revolution” journal had a negative impact on the internal state of the Society and strengthened the distrust of the results of his work from the government. In 1931–1932, the Society management unsuccessfully tried to make its work more popular, hold a plenum and re-registered a new charter. Results. However, at that time, the Central Committee of the CPSU(b) embarked on the path of reformatting the structure of societies and unions in the country and eliminating those of them that had exhausted their mobilization potential and did not meet the new ideological course. In addition to this circumstance, the rapid curtailment of the Society of Marxist historians by the end of 1932 was influenced by the position of the leadership of the Communist Academy and the death of M.N. Pokrovsky, the undisputed leader of Soviet historians.

Uncertain active contour model based on rough and fuzzy sets for auroral oval segmentation

Support Vector Data Description (SVDD) is an extremely hot topic issue in One-Class Classification (OCC), which has displayed outstanding performance in dealing with many novelty detection problems. However, SVDD just takes the data description by the kernel-based distance among each instance into consideration rather than considering the distribution of the data. Therefore, Fuzzy Support Vector Data Description (Fuzzy-SVDD) has been developed to distribute a fuzzy membership to each input sample so that different samples cause different contributions to classification boundary. The majority of the methods in Fuzzy-SVDD are based on the sample density, but there are remaining two problems. These density-based Fuzzy-SVDD methods would decrease the contribution of support vectors (SVs) in low densities. What is more, these methods cannot get a precise density when there are few target samples. These two problems would lead to a poor classification boundary. To overcome these drawbacks, a novel method called Boundary-based Fuzzy-SVDD (BF-SVDD) is proposed in this paper. BF-SVDD uses a new definition called local–global center distance to search for the samples near the boundary. Then, it enhances fuzzy memberships of these samples because they carry more significant information for the decision boundary than other data. The contribution of this paper can be summarized into three main points. First a novel concept called local–global center distances is proposed to find the SVs better. Second, fuzzy memberships with local–global center distance make SVs more informative to create the decision boundary. Furthermore, the experiments based on University of California, Irvine and Knowledge Extraction based on Evolutionary Learning also show that the proposed method has excellent performances. Even for the minority class in imbalance data sets, the proposed method can also have a good classification.

Despite the fact that machine learning approaches have been proven to be useful in the diagnosis of rotating machinery, there is a key assumption that all the data are distributed in a same manner. However, given the varying working conditions, this assumption is difficult to maintain in real-world settings, and it will result in unsupervised and domain shift issues, increasing the difficulty of fault diagnosis. Aimed at these issues, the adversarial domain adaptation method has provided a way to solve these problems caused by changeable working parameters. Due to the good embeddedness with deep learning network, and applicability of unsupervised learning, the adversarial domain adaptation has been widely concerned. Most of the adversarial domain adaptation based methods focus on how to adapt the source and target domain data to improve the fault diagnosis performance under variable working conditions. However, these methods only focus on the adaptation of inter-domain data, and ignore the separation of inter-class data, thereby limiting the improvement of fault diagnosis performance. In this study, we employ the silhouette coefficient to regularize the adversarial domain adaptation network in order to extract domain-invariant features while maintaining inter-class data separability. To enhance fault diagnostic performance, the proposed method has the ability to obtain a better classification boundary while achieving the alignment of the cross-domain in an unsupervised way. The experiments are designed via the CWRU dataset. The results suggest that the proposed method is effective to improve the adversarial domain adaptation-based framework in fault diagnosis.

The article discusses information technology for classifying data using space reduction methods to the level of data visualization. The components of the steps of using information technology are discussed in detail. The basis is the reduction of the multidimensional feature space into a space that can be visualized. The next step is the construction of piecewise linear class separators by the user on the training set. Further, the borders are projected into the original space of multidimensional objects. Thus, the boundaries of classes are determined, which is in the proposed technology a trained classification system. The advantage is the construction of a flexible non-linear classification system using piecewise linear separators. The proposed method is based on the use of visualization data. A distinctive feature of information technology is that the construction of class delimiters is done by user. Based on visual data analysis, user determines the location of class boundaries and spaces. Thus, technology provides user with convenient tools for data analyzing and classifying.

In this letter, we propose a novel method for inshore ship detection via ship head classification and body boundary determination. Compared with some traditional ship head detection methods depending on accurate ship head segmentation, we generate novel ship head features in the transformed domain of polar coordinate, where the ship heads have an approximate trapezoid shape and can be more easily detected. Then, these features are used in the classification based on support vector machine to detect the ship head candidates, and give the important information of initial ship head direction. Next, the surrounding consistent line segments are utilized to refine the ship direction, and the ship boundary is determined based on the saliency of directional gradient information symmetrical about the ship body. Finally, the context information of sea areas is introduced to remove false alarms. Experimental results show that the proposed method can accurately and robustly detect the inshore ships in high-resolution optical remote sensing images.

Purpose: Ribonucleic Acid Sequencing (RNA-Seq) is a technique that allows an efficient genome-wide analysis of gene expressions. Such analysis is a strategy for identifying hidden patterns in data, and those related to cancer-specific biomarkers. Prior analyses without samples of different cancer kinds used RNA-Seq data from the same type of cancer as the positive and negative samples. Therefore, different cancer types must be evaluated to uncover differentially expressed genes and perform multiple cancer classifications. Problem: Since gene expression reflects both the genetic make-up of an organism and the biochemical activities occurring in tissue and cells, it can be crucial in the early identification of cancer. The aim of this study is to classify the RNA-Sequence data into five different cancer forms, such as LUAD, BRCA, KIRC, LUSC, and UCEC, through an ensemble approach of machine learning algorithms. RNA-Seq data for five different cancer types from the UCI Machine Learning Repository are examined in this research. Methods: As a first step, the relevant features of RNA-Seq are extricated using Principal Component Analysis (PCA). Then, the extricated features are given to the ensemble of machine learning classifiers to classify the type of cancer. The ensemble of classifiers is built using Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN). Results: The results demonstrated that the proposed ensemble classifier outperformed the existing machine-learning approaches with an accuracy of 99.59%.

Growing ensemble of linear classifiers uses the 'divide and conquer' strategy in pattern recognition tasks. Performing the competitive learning the feature space is divided into subregions where linear classifiers are constructed. The structure of the ensemble is growing during the training and it is self determined. The overall output of the ensemble is the output of a winning member. The method is not bound to a specific type of classifier. According to the experimental results achieved on artificial and real world datasets the algorithm performs comparably to Gaussian SVM. Due to the simple nature of the decision boundary, knowledge retrieval procedures can be applied.

Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we investigate an approach based on augmented image features by incorporating curvelet transform and neural network (MLP) ensemble for classification. A simple Random Subspace (RS) ensemble offers satisfactory performance, which contains a set of base MLP classifiers trained with subsets of attributes randomly drawn from the combined features of curvelet coefficients and original Subcellular Location Features (SLF). An MLP ensemble with Random Linear Oracle (RLO) can further improve the performance by replacing a base classifier with a miniensemble, which consists of a pair of base classifiers and a fixed, randomly created oracle that selects between them. With the benchmarking 2D HeLa images, our experiments show the effectiveness of the proposed approach. The RS-MLP ensemble offers the classification rate 95% while the RS-RLO ensemble gives 95.7% accuracy, which compares sharply with the previously published benchmarking result 84%.

A novel technique, combining the feature extraction mechanisms of a convolutional neural network (CNN) with the classification method of a radial basis probability neural network (RBPNN), is proposed for small sample set modeling and feature knowledge embedding in multi-channel time-varying signal classification. This CNN-RBPNN consists of a signal input layer, signal feature parallel extraction and integration units, and an RBPNN classifier. Each channel signal in a feature extraction unit corresponds to a 1D CNN. The extracted features are represented as feature vectors, and these vectors constitute a comprehensive feature matrix. The RBPNN classifier was designed using signal feature embedding mechanism based on radial basis kernels and the property of combining pattern subclasses into pattern classes to form complex class boundaries. A dynamic clustering algorithm was used to divide each pattern class sample into several subclasses. Typical signal samples in each pattern subclass were designated as kernel centers, in order to achieve signal categories features embedding. This process was also used to determine the number of nodes in the RBPN layer. The RBPN layer outputs were selectively summed in the pattern layer according to kernel center category, which can generate irregular class boundaries, reducing the overlap among different pattern class boundary. The proposed CNN-RBPNN replaces the full-connection layer and classifier unit of conventional CNN with RBPNN, which can extract and represent signals distribution features and structural properties, implement structural and data constraints. This can reduce the structural risks of small sample set modeling. In this study, the properties of CNN-RBPNN are analyzed and an integrated learning algorithm is proposed. An experiment was conducted using 12-lead ECG signals in a seven-classification in the case of small sample set. Results demonstrated that, the correct recognition rate is 5.7% higher than other methods in the experiment, the performance evaluation index also showed significant improvement.

본 논문에서는 마이크로어레이 (microarray) 자료에 판별분석을 적용 시 나타나는 고차원 및 소표본 문제의 해결방법으로서 직교요인을 새로운 특징변수로 사용한 비모수적 국소선형 로지스틱 판별분석을 제안한다. 제안된 방법은 국소우도에 기반한 것으로서 다범주 판별분석에 적용될 수 있으며, 고려된 직교인자는 주성분 요인, 부분최소제곱 요인, 인자분석 요인 등이다. 대표적인 두 가지 실제 마이크로어레이 자료에 적용한 결과 직교요인들 중에서 부분최소제곱 요인을 특징변수로 사용한 경우 고전적인 통계적 판별분석보다 향상된 분류 능력을 나타내고 있음을 확인하였다. The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

In general, there are two kinds of linear classification methods: one is MSE, and the other is FLD. Because linear methods are not sufficient to analyze the data with nonlinear patterns, the nonlinear methods KMSE and KFD are hence developed from MSE and FLD, respectively. Both transform the instances from the original attribute space to the high-dimensional feature space and then linear methods are applied. The objective of FLD and KFD is to find the directions on which the projection of training instances can provide the maximal separability of classes. FLD and KFD are known to be inefficient for datasets with a large amount of attributes and instances, respectively. To improve the computing efficiency, we use MSE for linear classification problems. However, MSE, like SVM, can use only the one-against-one or the one-against-the-rest approach to solve the multi-class problems. Both are inefficient compared to FLD and KFD where only one model is built to discriminate multiple classes simultaneously. Thus, we develop the multi-class MSE with Sherman-Woodbury formula to improve the computation efficiency. It can deal with multiple classes simultaneously by a class-labeling scheme. The different class-labeling schemes are determined by the Gram-Schmidt process. The nonlinear application, multi-class KMSE, is also developed from the multi-class MSE. Then, a simulated example is used to show how the proposed method works and to visualize the meaning of the class-labeling scheme. Finally, two real-world datasets are used for comparing the proposed method with other conventional methods.

Predicting Students at Risk of Early Dropping Out from Course Using Ensemble Classification Methods

In this paper, we propose a novel approach for dynamic feature selection to be applied in classifier ensembles. This method selects the best attribute subsets for an individual instance or a group of instances of an input dataset. Hence, each testing instance is classified using a unique feature subset in the classification process. The main aim of this paper is to extend a dynamic feature selection method that was proposed for single classifiers, adjusting this approach to be used in classifier ensembles. In order to validate our proposed method, an empirical analysis is conducted to investigate the effectiveness of approach compared to existing ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed method to the existing ensemble methods.