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The One-vs-One (OVO) scheme that decomposes the original more complicated problem into as many as possible pairs of easier-to-solve binary sub-problems is one of the most popular techniques for handling multi-class classification problems. In this paper, we propose an improved Dynamic Ensemble Selection (DES) procedure, which aims to enhance the OVO scheme via dynamically selecting a group of appropriate heterogeneous classifiers in each sub-problem for each query example. To do so, twenty heterogeneous classification algorithms are selected to obtain a set of candidate classifiers for each sub-problem derived from the OVO decomposition. Then, a simple yet efficient DES procedure is developed to execute the dynamic selection for each query example in each sub-problem. Finally, all the selected binary heterogeneous ensembles are combined by using majority voting to obtain the final output class. To evaluate the proposed method, we carry out a series of experiments on twenty datasets selected from the KEEL repository. The results supported by proper statistical tests demonstrate the validity and effectiveness of our proposed method, compared with state-of-the-art methods for OVO-based multi-class classification. [ABSTRACT FROM AUTHOR]

Multi-class imbalance learning problems suffering from the different distribution of classes occur in many real-world applications. One-versus-One (OVO) decomposition strategy is a common and useful technique used to address multi-class classification problems, which consists in dividing the original multi-class problem into all binary class sub-problems. The effort to reduce the effect of non-competent classifiers has proven to be a useful way of improving the performance in the OVO scheme. However, these approaches might not be effective for imbalance scenarios, since they are based on standard biased learning procedures. On this account, we propose a novel approach named Distance-based Relative Competence Weighting with Adaptive Synthetic Example Generation (DRCW-ASEG), which properly addresses the synergy between imbalance learning and dynamic classifier weighting in OVO scheme. This new proposed algorithm aims to dynamically produce synthetic examples of minority classes in the stage of dynamic weighting process. We develop a thorough experimental study in order to verify the benefits of the proposed algorithm considering different base binary classifiers. [ABSTRACT FROM AUTHOR]

Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose. In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility. Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 – 0.966), 0.909 (95 % CI: 0.829 – 0.988) and 0.839 (95 % CI: 0.709 – 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas. The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment. [ABSTRACT FROM AUTHOR]

Solar energy constitutes an effective supplement to traditional energy sources. However, photovoltaic power generation (PVPG) is strongly weather-dependent, and thus highly intermittent. High-precision forecasting of PVPG forms the basis of the production, transmission, and distribution of electricity, ensuring the stability and reliability of power systems. In this work, we propose a deep learning based framework for accurate PVPG forecasting. In particular, taking advantage of the long short-term memory (LSTM) network in solving sequential-data based regression problems, this paper considers the specific domain knowledge of PV and proposes a physics-constrained LSTM (PC-LSTM) to forecast the hourly day-ahead PVPG. It aims to overcome the shortcoming of recent machine learning algorithms that are applied based only on massive data, and thus easily producing unreasonable forecasts. Real-life PV datasets are adopted to evaluate the feasibility and effectiveness of the models. Sensitivity analysis is conducted for the selection of input feature variables based on a two-stage hybrid method. The results indicate that the proposed PC-LSTM model possesses stronger forecasting capability than the standard LSTM model. It is more robust against PVPG forecasting, and more suitable for PVPG forecasting with sparse data in practice. The PC-LSTM model also demonstrates superior performance with higher accuracy of PVPG forecasting compared to conventional machine learning and statistical methods. • Domain knowledge of PV is firstly considered into the deep-learning model. • A two-stage hybrid method is proposed to select the input feature variables. • PC-LSTM is more robust against PV power output forecasting than the basic LSTM. • PC-LSTM has advantages in the forecasting of PV power generation with sparse data. [ABSTRACT FROM AUTHOR]

Class imbalance learning is one of the most important topics in the field of machine learning and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the common method to handle this issue. The main shortcomings of the classic SMOTE and its variants is the interpolation of potential noise and unrepresentative examples. This paper is devoted to proposing a novel parameter-free SMOTE mechanism to produce sufficient representative synthetic examples while avoiding interpolating noisy examples. Specifically, two types of minority class examples are defined, namely boundary and safe minority examples. The synthetic examples generation procedure fully reflects the characteristics of the minority class examples with filling the region dominated by the minority class and expanding the margin of the minority class. To verify the effectiveness and robustness of the proposed method, a thorough experimental study on forty datasets selected from real-world applications is carried out. The experimental results indicate that our proposed method is competitive to the classic SMOTE and its state-of-the-art variants. [ABSTRACT FROM AUTHOR]