To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

In this study, [Sr_0.99Eu_0.01]₃MgSi₂O₈ phosphors were sintered at 1200–1400 °C for 1–5 h by using the solid-state reaction method. The crystallinity and morphology of these phosphors were characterized through X-ray diffraction analysis and field-emission scanning electron microscopy, respectively, to determine their luminescence. The photoluminescence properties, including the excitation and emission properties, of the prepared phosphors were investigated through fluorescence spectrophotometry. The α-Sr₂SiO₄, Sr₂MgSi₂O₇, and Sr₃MgSi₂O₈ phases coexisted in the [Sr_0.99Eu_0.01]₃MgSi₂O₈ phosphors, which were synthesized at low temperatures. The particles of these phosphors had many fine hairs on their surface and resembled Clavularia viridis, which is a coral species. Transmission electron microscopy and energy dispersive X-ray spectroscopy indicated that the fine hairs contained the Sr₂SiO₄ and Sr₂MgSi₂O₇ phases. However, when the [Sr_0.99Eu_0.01]₃MgSi₂O₈ phosphors were sintered at 1400 °C, the Sr₃MgSi₂O₈ phase was observed, and the Eu²⁺-doped Sr₃MgSi₂O₈ phase dominated the only broad emission band, which had a central wavelength of 457 nm (blue light). The emission peaks at this wavelength were attributed to the 4f⁶5d¹–4f⁷ transition at the Sr²⁺(I) site, where Sr²⁺ was substituted by Eu²⁺. The average decay time of the synthesized phosphors was calculated to be 1.197 ms. The aforementioned results indicate that [Sr_0.99Eu_0.01]₃MgSi₂O₈ can be used as a blue-emitting phosphor in ultraviolet-excited white light-emitting diodes.

In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

Air pollution has an ongoing devastating impact on the planet, damaging ecosystems, depleting natural resources, and endangering human health. This paper proposes a new intelligent algorithm that includes parameter optimization and decision rules to forecast and analyze of urban air quality. Through analysis of 24-h daily air quality data provided by the Beijing Air Quality Monitoring Station, simulated annealing (SA) and a decision tree (DT) emerge as the key factors. We prove that in the investigated algorithm, SA and DT can be used to make decision rules and achieve better accuracy for classification. We find that SA can be used to adjust the best parameter settings for the DT. Simulation results show that the accuracy of the proposed algorithm for classification is far better than other existing approaches.

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features. The results show that the proposed algorithm BLSMOTE + K-means + SVM is superior to other traditional machine learning methods in classification and various performance indicators. The BLSMOTE + K-means + DT generates decision rules for the dry bean dataset and the the obesity levels dataset, and the BLSMOTE + K-means + RF ranks the importance of explanatory variables. These experimental results can provide scientific evidence for decision-makers.

Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.