Caracterização de alunos em ambientes de ensino online: estendendo o uso da DAMICORE para minerar dados educacionais

Moro, Luis F. de S.; Rodrigues, Carla Larissa Halum; Andrade, Fernando R. H.; Delbem, Alexandre C. B.; Isotani, Seiji

doi:10.11606/d.55.2015.tde-14092015-164510

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…DAMICORE requires no parameters setup to run (although some execution options may improve its performance). DAMICORE has been successfully employed in a variety of fields, for example, software-hardware co-design [16,17,18], compiler optimization [19,20], student profiling in e-learning environments [21,22], identification of phytopathology from sensor data [18], systematic literature review, identification of cross-cut concerns [23], electrical distribution systems [24], and novel methods in bioinformatics [25]. Feature Sensitivity Analysis.…”

Section: Data Mining Methodology Adopted -Fs-opamentioning

confidence: 99%

“…The former only works on the objective space for the exclusive purpose of space reduction to determine the essential objective set [9]. Moreover, the FS-OPA preserves the original variable space, which favours non-expert human interpretability (relevant for some classes of real-world problems); it also has a relatively low-time complexity and has shown beneficial results when applied to small datasets [10,[16][17][18][19][20][21][22][23].…”

Section: Comparison Of Fs-opa With Nl-mvu-pca For Maops Data-driven S...mentioning

confidence: 99%

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Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems

Gaspar‐Cunha¹,

Costa²,

Monaco³

et al. 2023

Lecture Notes in Computer Science

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The use Multi-Objective Evolutionary Algorithms (MOEAs) to solve real-world multi-objective optimization problems often finds a problem designated by the curse of dimensionality. This is mainly because the progression of the algorithm along successive generations is based on non-dominance relations that practically do not exist when the number of objectives is high. Also, the existence of many objectives makes the choice of a solution to the problem under study very difficult. Several methods have been proposed in the literature to reduce the number of objectives to use during the optimization process. In the present work, a methodology to reduce the number of objectives is proposed. This method is based on DAMICORE (DAta MIning of COde REpositories), a machine-learning algorithm proposed by the authors. A theoretical comparison with a similar machine learning approach is made, pointing out some advantages of using the proposed algorithm using a benchmark problem designated by DTLZ5. Also, a real problem is used to show the effectiveness of the methodology.

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Section: Data Mining Methodology Adopted -Fs-opamentioning

confidence: 99%

Section: Comparison Of Fs-opa With Nl-mvu-pca For Maops Data-driven S...mentioning

confidence: 99%

Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems

Gaspar‐Cunha¹,

Costa²,

Monaco³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Naturally, pre-processing steps and some execution options may improve the DAMICORE performance. Its success in such a challenge has been checked for problems in a variety of fields, such as software-hardware co-design [23][24][25], compiler optimization [26], student profiling in e-learning environments [27,28], identification of phytopathology from sensor data [29], systematic literature review, identification of cross-cut concerns [30], and electrical distribution systems [31].…”

Section: Fs-opamentioning

confidence: 99%

“…The former works on the objective space for space reduction to determine the essential objective set [12]. FS-OPA also has other properties that are relevant for some classes of real-world problems: (i) it preserves the original variable space, which favours non-experts interpretability; (ii) it works with any data type (continuous, discrete, categorical-not only ordinal, but also nominal data, addressed by Bandaru et al [4]) and mixed types (proper for multiple heterogeneous databases with observed data); (iii) it has a relatively low time complexity; (iv) and, finally, it has generated applicable models when applied to learn from small datasets [17,[23][24][25][26][27][28][29][30][31].…”

Section: Comparison Of Fs-opa With Nl-mvu-pca For Maops Data-driven S...mentioning

confidence: 99%

Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives

Gaspar‐Cunha

Costa

Monaco

et al. 2023

MCA

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Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.

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Preliminary study of Data Mining analysis of high speed Kymography and voice data

et al. 2018

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The use of high-speed laryngeal images technology contributes increasingly to promote greater objectivity in the characterization of laryngeal physiology, as well as, in the diagnosis and monitoring of laryngeal diseases. Vocal fold paralysis is still an unknown incidence, both in Brazil and in the rest of the World, its occurrence has become more frequent in medical and speech-pathologist clinics. Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information. In other words, Data Mining is able to identify patterns between information and grouping them according to some given criteria. Objective: To analyze high-speed kymographic images and voice signals with and without unilateral vocal fold paralysis using the data mining computerized system DAMICORE. Methodology: High-speed kymography and acoustic voice signals of subjects with and without unilateral vocal fold paralysis were analyzed by a computerized data mining tool, the DAMICORE system. Results: Data mining analysis for High-speed kymography of healthy individuals gather the groups by sex, while images from subjects with unilateral vocal fold paralysis were gathered by contrast of colors and by light incidence in the images. For voice signals, the technique considered the presence of external noise as a criterion for gathering groups. Conclusion: We concluded that DAMICORE it is a promising tool for kymographic images and voice signal data mining analysis. This tool shown high sensibility to noise in acoustical signals. Consequently, future research should consider this characteristic.

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Caracterização de alunos em ambientes de ensino online: estendendo o uso da DAMICORE para minerar dados educacionais

Cited by 3 publications

References 27 publications

Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems

Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems

Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives

Preliminary study of Data Mining analysis of high speed Kymography and voice data

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