Free cookie consent management tool by TermsFeed Policy Generator

Research groups

GC25 Knowledge Discovery and Intelligent Systems in Biomedicine

The Research Group “Knowledge Discovery and Intelligent Systems in Biomedicine” is a consolidated group of the IMIBIC. Our research team incorporates 10 researches and 8 PhD students. Our research areas cover two main fields: knowledge discovery and data mining, and the application of artificial intelligence techniques for the industrial development of intelligent systems. We have experience in basic research on the areas of big data, machine learning, soft computing and optimization techniques. At the same time, we have proven experience on the use of these techniques over diverse application domains and, lately, we have focused on applying them to Biomedicine field.

Our line of work for the next years focuses on the development of data analysis methodologies/proposals for solving complex problems in Biomedicine field with great social relevance, such as melanoma prediction, alternative splicing, prediction and description of pathologies related to arterial hypertension, among others. Data analysis techniques play a fundamental role in medical diagnosis, especially with the growth of precision medicine and individualized prognosis; thus, early diagnosis models suppose a great advantage for both the patient and the health-care system, whereas, the models we obtain can shed light on the understanding of diseases.

The principal investigator of the group Sebastian Ventura Soto and the rest of the members collaborate with the PAIDI TIC-222 Scientific group. Other collaborations include highly competitive research groups both on the national and international levels.

Lines of Research

Predictive models are developed with the aim of estimating an output or a set of output values given a set of input features. Depending of the type of the outputs, these models are mainly categorized into classification (discrete outputs) and regression (continuous outputs).

Traditional classification and regression problems estimate an unique output value from a unique input vector, but, in the last years, more flexible representations of the input (multi-instance, multi-view) and output spaces (multi-label, multi-target) have been defined. Our studies in this field are based on the development of a wide range of predictive models for both classic problems and also problems with a more flexible representation of the input and output spaces.

Some of the studies of our research group are to directly apply these models to a wide range of real problems in biomedicine such as the prediction of risk of diabetes in patients and the diagnosis from clinical texts using multi-label classification.

Pattern mining aims at extracting and describing elements that are somehow related in a database. Patterns, as the key element in Data Analytics, represent any type of homogeneity and regularity in data and they serve as good descriptors of intrinsic and important properties of data. Our studies in this research field are focused on extracting knowledge (in the form of relationships) from the scratch and discovering useful information associated to specific variables of interest for the application field. Our research group has a broad background in different type of patterns, including frequent/infrequent patterns defined on discrete/continuous domains, and defined on different data types such as relational (as well as multirelational) data, sequential data and data defined on ambiguous domains. Last but not least, our research group has developed a wide range of algorithms for mining patterns with regard to a single (or multiple) target variable or variable of interest, including subgroup discovery approaches and algorithms for the discovery of exceptional models, among others.

In the context of the big data era, information systems produce a continuous flow of massive collections of data surpassing storage and computation capabilities of traditional knowledge extraction methods. Big data is characterized by its properties which include volume, velocity, variety, veracity, variability, visualization, and value.

In recent years, researchers have focused predominantly on the scalability of data mining algorithms to address the ever-increasing data volume. Distributed computing platforms such as Apache Hadoop and Spark implement the MapReduce programming model to scale out state of the art data mining algorithms to ever increasing heterogenous data volumes. This issue is especially challenging in the biomedical domain, comprising massive amounts of information from many data sources. Efficient and effective integration of all the available data to infer meaningful and accurate conclusions is not straightforward. Our research group has developed scalable algorithms for big data collections, adapted to the needs of the 21st-century information systems.

Workflow technology brings a representation framework to conduct data analysis closer to the application domain, hiding computational and execution requirements, and enabling the development of complex processes for knowledge extraction from heterogeneous data. Thus, workflows are a high-level mechanism to automate and describe processes as a set of activities that work together to produce a desired outcome. In data science, the application of workflows to data-intensive tasks faces significant challenges, not only referred to the decomposition of knowledge extraction methods into processes and activities, but also to the adaptation and arrangement of data-intensive, low-level algorithmic procedures. The development of workflow-based Big Data solutions requires the analysis of new parallelization solutions for data mining algorithms; their execution on distributed platforms, both clusters (e.g. Hadoop) and cloud-based systems (e.g. Azure); the reuse of processes and workflows among different application domains and problems like biomedicine or education; the optimisation of high-performance processes for the execution of data-intensive workflows at runtime; or data transformation of complex data structures (e.g. data streams). With the aim of democratizing data science over industrial environments, our research group is working on the construction of workflow-based solutions for improving the deployment and reusability of data mining algorithms and knowledge discovery techniques.

Networks

    SEBASENet - Network of Excellence in Search Based Software Engineering 

    Red de Excelencia en Big Data y Análisis de Datos Escalable

    Teoría y Aplicaciones de Minería de Datos

    PAIDI TIC-122

Key Words

  • biomedical data science
  • medical image
  • machine learning
  • big data
  • descriptive models
  • predictive models
  • healthcare data analytics
  • radiomics
  • deep learning
  • clustering
  • pattern mining
  • classification and regression

Información Adicional

Our website: Knowledge Discovery and Intelligent Systems

Projects

PROJECTS IN 2023

Ongoing projects

National

SEBASTIÁN VENTURA SOTO. iDATA-MP - programa de Datos de IMPaCT. Funding Agency: INSTITUTO DE SALUD CARLOS III. Reference: IMP/00019-IMIBIC

PROJECTS IN 2022

Ongoing projects

National

SEBASTIÁN VENTURA SOTO; iDATA-MP - programa de Datos de IMPaCT Funding Agency: INSTITUTO DE SALUD CARLOS III; Reference: IMP/00019-IMIBIC

Publications

Publications in 2023

Blázquez-Encinas R, García-Vioque V, Caro-Cuenca T, Moreno-Montilla MT, Mangili F, Alors-Pérez E, Ventura S, Herrera-Martínez AD, Moreno-Casado P, Calzado MA, Salvatierra A, Gálvez-Moreno MA, Fernandez-Cuesta L, Foll M, Luque RM, Alcala N, Pedraza-Arevalo S, Ibáñez-Costa A, Castaño JP. Altered splicing machinery in lung carcinoids unveils NOVA1, PRPF8 and SRSF10 as novel candidates to understand tumor biology and expand biomarker discovery. JOURNAL OF TRANSLATIONAL MEDICINE. 2023. DOI: 10.1186/s12967-023-04754-8.         IF: 7,4       Q1 D3

Porras JM, Lara JA, Romero C, Ventura S. A Case-Study Comparison of Machine Learning Approaches for Predicting Student's Dropout from Multiple Online Educational Entities. ALGORITHMS. 2023. DOI: 10.3390/a16120554.         IF: 2,3       Q3 D

Trasierras AM, Luna JM, Ventura S. A contrast set mining based approach for cancer subtype analysis. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2023. DOI: 10.1016/j.artmed.2023.102590.         IF: 7,5       Q1 D2

Perez E, Ventura S. A framework to build accurate Convolutional Neural Network models for melanoma diagnosis. KNOWLEDGE-BASED SYSTEMS. 2023. DOI: 10.1016/j.knosys.2022.110157.         IF: 8,8       Q1 D2

Ordonez-Avila R, Reyes NS, Meza J, Ventura S. Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review. HELIYON. 2023. DOI: 10.1016/j.heliyon.2023.e13939.         IF: 4       Q2 D4

Luna JM, Kiran RU, Fournier-Viger P, Ventura S. Efficient mining of top-k high utility itemsets through genetic algorithms. INFORMATION SCIENCES. 2023. 529-553. DOI: 10.1016/j.ins.2022.12.092.         IF: 8,1       Q1 D1

Barbudo R, Ventura S, Romero JR. Eight years of AutoML: categorisation, review and trends. KNOWLEDGE AND INFORMATION SYSTEMS. 2023. 5097-5149. DOI: 10.1007/s10115-023-01935-1.         IF: 2,7       Q3 D

Cachi PG, Ventura S, Cios KJ. Improving Spiking Neural Network Performance with Auxiliary Learning. MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2023. 1010-1022. DOI: 10.3390/make5030052.         IF: 3,9       Q2 D4

Reyes O, Pèrez E. Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY. 2023. 1-21. DOI: 10.1145/3625237.         IF: 5       Q2 D4

Pérez E, Reyes O. Performing Melanoma Diagnosis by an Effective Multi-view Convolutional Network Architecture. INTERNATIONAL JOURNAL OF COMPUTER VISION. 2023. 3094-3117. DOI: 10.1007/s11263-023-01848-0.         IF: 19,5       Q1 D1

Perez E, Ventura S. Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2023. DOI: 10.1016/j.artmed.2023.102556.         IF: 7,5       Q1 D2

Pedraza-Arevalo S, Alors-Perez E, Blazquez-Encinas R, Herrera-Martinez AD, Jimenz-Vacas JM, Fuentes-Fayos AC, Reyes O, Ventura S, Sanchez-Sanchez R, Ortega-Salas R, Serrano-Blanch R, Galvez-Moreno MA, Gahete MD, Ibanez-Costa A, Luque RM, Castano JP. Spliceosomic dysregulation unveils NOVA1 as a candidate actionable therapeutic target in pancreatic neuroendocrine tumors. TRANSLATIONAL RESEARCH. 2023. 63-73. DOI: 10.1016/j.trsl.2022.07.005.         IF: 7,8       Q1 D2

Publications in 2022

Yacoub MF, Maghawry HA, Helal NA, Ventura S, Gharib TF. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS. 2022. DOI: 10.14569/IJACSA.2022.01304101

Perez E, Ventura S. NEURAL COMPUTING & APPLICATIONS. 2022. DOI: 10.1007/s00521-021-06655-7

Moyano JM, Ventura S. INFORMATION FUSION. 2022. DOI: 10.1016/j.inffus.2021.07.005

Al-Twijri MI, Luna JM, Herrera F, Ventura S. COGNITIVE COMPUTATION. 2022. DOI: 10.1007/s12559-022-10015-5

Esteban A, Zafra A, Ventura S. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY. 2022. DOI: 10.1002/widm.1471

Bowen-Mendoza L, Pinargote-Ortega M, Meza J, Ventura S. JOURNAL OF COMPUTING IN HIGHER EDUCATION. 2022. DOI: 10.1007/s12528-021-09297-9

Barron MA, Luna JM, Ventura S. IEEE ACCESS. 2022. DOI: 10.1109/ACCESS.2022.3225435

Trasierras AM, Luna JM, Ventura S. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. 2022. DOI: 10.1002/int.22503

Maqsood R, Ceravolo P, Romero C, Ventura S. KNOWLEDGE AND INFORMATION SYSTEMS. 2022. DOI: 10.1007/s10115-022-01674-9

Moyano JM, Luna JM, Ventura S. IEEE ACCESS. 2022. DOI: 10.1109/ACCESS.2022.3192642

Luna JM, Fardoun HM, Padillo F, Romero C, Ventura S. INTERACTIVE LEARNING ENVIRONMENTS. 2022. DOI: 10.1080/10494820.2019.1643742

Publications in 2021

Quintero-Dominguez LA, Morell C, Ventura S. A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming. EXPERT SYSTEMS WITH APPLICATIONS. 202  168():-. DOI: 10.1016/j.eswa.2020.114263.                   

IF: 6,954         Q1     D1

Luque C, Luna JM, Ventura S. A semantically enriched text mining system for clinical decision support. COMPUTATIONAL INTELLIGENCE. 2021.  37(4):1545-1570. DOI: 10.1111/coin.1232                   

IF: 2,33         Q3    

Frias M, Moyano JM, Rivero-Juarez A, Luna JM, Camacho A, Fardoun HM, Machuca I, Al-Twijri M, Rivero A, Ventura S. Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach. JOURNAL OF MEDICAL INTERNET RESEARCH. 2021.  23(2):-. DOI: 10.2196/18766.                   

IF: 5,428         Q1     D1

Perez E, Reyes O, Ventura S. Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study. MEDICAL IMAGE ANALYSIS. 2021.  67():-. DOI: 10.1016/j.media.2020.101858.                   

IF: 8,545         Q1     D1

Barbudo R, Ramirez A, Servant F, Romero JR. GEML: A grammar-based evolutionary machine learning approach for design-pattern detection. JOURNAL OF SYSTEMS AND SOFTWARE. 2021.  175():-. DOI: 10.1016/j.jss.2021.110919.                   

IF: 2,829         Q1     D3

Perez E, Ventura S. Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules. CANCERS. 2021.  13(19):-. DOI: 10.3390/cancers13194974.                    IF: 6,639         Q1     D3

Pinargote-Ortega M, Bowen-Mendoza L, Meza J, Ventura S. Peer assessment using soft computing techniques. JOURNAL OF COMPUTING IN HIGHER EDUCATION. 2021.  33(3):684-726. DOI: 10.1007/s12528-021-09296-w.                   

IF: 2,627         Q2     D5

Moyano JM, Reyes O, Fardoun HM, Ventura S. Performing multi-target regression via gene expression programming- based ensemble models. NEUROCOMPUTING. 2021.  432():275-287. DOI: 10.1016/j.neucom.2020.12.060.                   

IF: 5,719         Q1     D3

Alors-Perez E, Blazquez-Encinas R, Alcala S, Viyuela-Garcia C, Pedraza-Arevalo S, Herrero-Aguayo V, Jimenez-Vacas JM, Mafficini A, Sanchez-Frias ME, Cano MT, Abollo-Jimenez F, Marin-Sanz JA, Cabezas-Sainz P, Lawlor RT, Luchini C, Sanchez L, Sanchez-Hidalgo JM, Ventura S, Martin-Hijano L, Gahete MD, Scarpa A, Arjona-Sanchez A, Ibanez-Costa A, Sainz B, Luque RM, Castano JP. Dysregulated splicing factor SF3B1 unveils a dual therapeutic vulnerability to target pancreatic cancer cells and cancer stem cells with an anti-splicing drug. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH. 202  40(1):-. DOI: 10.1186/s13046-021-02153-9.                   

IF: 11,161         Q1     D1

Luque-Tevar M, Perez-Sanchez C, Patino-Trives AM, Barbarroja N, de la Rosa IA, Abalos-Aguilera MC, Marin-Sanz JA, Ruiz-Vilchez D, Ortega-Castro R, Font P, Lopez-Medina C, Romero-Gomez M, Rodriguez-Escalera C, Perez-Venegas J, Ruiz-Montesinos MD, Dominguez C, Romero-Barco C, Fernandez-Nebro A, Mena-Vazquez N, Marenco JL, Uceda-Montanez J, Toledo-Coello MD, Aguirre MA, Escudero-Contreras A, Collantes-Estevez E, Lopez-Pedrera C. Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis. FRONTIERS IN IMMUNOLOGY. 2021.  12():-. DOI: 10.3389/fimmu.2021.63166                   

IF: 7,561         Q1     D2

Autores, Cachi PG, Ventura S, Cios KJ. CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE. 2021.  15():-. DOI: 10.3389/fncom.2021.627567.                   

IF: 2,38         Q2     D5

Fournier-Viger P, Yang P, Kiran RU, Ventura S, Luna JM. Mining local periodic patterns in a discrete sequence. INFORMATION SCIENCES. 2021.  544():519-548. DOI: 10.1016/j.ins.2020.09.044.                   

IF: 6,795         Q1     D2

Publications in 2020

Reyes O, Perez E, Luque RM, Castano J, Ventura S. A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2020. 108 ():- DOI: 10.1016/j.artmed.2020.101950 
IF: 4,383 Q: 1

Moyano JM, Gibaja EL, Cios KJ, Ventura S. Combining multi-label classifiers based on projections of the output space using Evolutionary algorithms. KNOWLEDGE-BASED SYSTEMS. 2020. 196 ():- DOI: 10.1016/j.knosys.2020.105770 
IF: 5,921 Q: 1

Gonzalez-Lopez J, Ventura S, Cano A. Distributed multi-label feature selection using individual mutual information measures. KNOWLEDGE-BASED SYSTEMS. 2020. 188 ():- DOI: 10.1016/j.knosys.2019.105052 
IF: 5,921 Q: 1

Gonzalez-Lopez J, Ventura S, Cano A. Distributed Selection of Continuous Features in Multilabel Classification Using Mutual Information. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. 2020. 31 (7):2280-2293 DOI: 10.1109/TNNLS.2019.2944298 
IF: 8,793 Q: 1 D: 1

Jimenez-Vacas JM, Herrero-Aguayo V, Montero-Hidalgo AJ, Gomez-Gomez E, Fuentes-Fayos AC, Leon-Gonzalez AJ, Saez-Martinez P, Alors-Perez E, Pedraza-Arevalo S, Gonzalez-Serrano T, Reyes O, Martinez-Lopez A, Sanchez-Sanchez R, Ventura S, Yubero-Serrano EM, R. Dysregulation of the splicing machinery is directly associated to aggressiveness of prostate cancer. EBIOMEDICINE. 2020. 51 ():- DOI: 10.1016/j.ebiom.2019.11.008 
IF: 5,736 Q: 1

Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY. 2020. 10 (3):- DOI: 10.1002/widm.1355 
IF: 4,476 Q: 1

Luna JM, Pechenizkiy M, Duivesteijn W, Ventura S. Exceptional in so Many Ways-Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios. IEEE ACCESS. 2020. 8 ():200982-200994 DOI: 10.1109/ACCESS.2020.3034885 
IF: 3,745 Q: 1

Luna JM, Fournier-Viger P, Ventura S. Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records. IEEE ACCESS. 2020. 8 ():134782-134799 DOI: 10.1109/ACCESS.2020.3010852 
IF: 3,745 Q: 1

Delgado-Osuna JA, Garcia-Martinez C, Gomez-Barbadillo J, Ventura S. Heuristics for interesting class association rule mining a colorectal cancer database. INFORMATION PROCESSING & MANAGEMENT. 2020. 57 (3):- DOI: 10.1016/j.ipm.2020.102207 
IF: 4,787 Q: 1 D: 1

Padillo F, Luna JM, Ventura S. LAC: Library for associative classification. KNOWLEDGE-BASED SYSTEMS. 2020. 193 ():- DOI: 10.1016/j.knosys.2019.105432 
IF: 5,921 Q: 1

Garcia-Martinez C, Ventura S. Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS. 2020. 13 (1):576-590 DOI: 10.2991/ijcis.d.200511.002 
IF: 1,838 Q: 3

Saeed-Ul H, Aljohani NR, Idrees N, Sarwar R, Nawaz R, Martinez-Camara E, Ventura S, Herrera F. Predicting literature's early impact with sentiment analysis in Twitter. KNOWLEDGE-BASED SYSTEMS. 2020. 192 ():- DOI: 10.1016/j.knosys.2019.105383 
IF: 5,921 Q: 1

Fuentes-Fayos AC, Vazquez-Borrego MC, Jimenez-Vacas JM, Bejarano L, Pedraza-Arevalo S, L-Lopez F, Blanco-Acevedo C, Sanchez-Sanchez R, Reyes O, Ventura S, Solivera J, Breunig JJ, Blasco MA, Gahete MD, Castano JP, Luque RM. Splicing machinery dysregulation drives glioblastoma development/aggressiveness: oncogenic role of SRSF3. BRAIN. 2020. 143 ():3273-3293 DOI: 10.1093/brain/awaa273 
IF: 11,337 Q: 1 D: 1

Hassan SU, Aljohani NR, Shabbir M, Ali U, Iqbal S, Sarwar R, Martinez-Camara E, Ventura S, Herrera F. Tweet Coupling: a social media methodology for clustering scientific publications. SCIENTOMETRICS. 2020. 124 (2):973-991 DOI: 10.1007/s11192-020-03499-1 
IF: 2,867 Q: 1

Lopez-Zambrano, J; Lara, JA; Romero, C. Towards portability of models for predicting students’ final performance in university courses starting from Moodle Logs . APPLIED SCIENCES-BASEL. 2020. 10 (1):- DOI: 10.3390/app10010354 
IF: 2,474 Q: 2

Cerezo, R; Bogarin, A; Esteban, M; Romero, C. Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education . JOURNAL OF COMPUTING IN HIGHER EDUCATION. 2020. 32 (1):- DOI: 10.1007/s12528-019-09225-y 
IF: 2,271 Q: 1

Publications in 2019

Ramirez A, Romero JR, Garcia-Martinez C, Ventura S; JCLEC-MO: A Java suite for solving many-objective optimization engineering problems. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. 2019. 81. 14-28. DOI: 10.1016/j.engappai.2019.02.003. 
IF: 3,526 Q: 1

Ramirez A, Romero JR, Ventura S; A survey of many-objective optimisation in search-based software engineering. JOURNAL OF SYSTEMS AND SOFTWARE. 2019. 149. 382-395. DOI: 10.1016/j.jss.2018.12.015. 
IF: 2,559 Q: 1

Luque C, Luna JM, Luque M, Ventura S; An advanced review on text mining in medicine. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY. 2019. 9. 3. DOI: 10.1002/widm.1302.
IF: 2,541 Q: 1

Luna JM, Fournier-Viger P, Ventura S; Frequent itemset mining: A 25 years review. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY. 2019. 0. DOI: 10.1002/widm.1329.
IF: 2,541 Q: 1

Romero C, Ventura S; Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance. IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES. 2019. 12. 2. 145-147. DOI: 10.1109/TLT.2019.2908106. 
IF: 2,315 Q: 1

Luna JM, Ondra M, Fardoun HM, Ventura S; Optimization of quality measures in association rule mining: an empirical study. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS. 2019. 12. 1. 59-78. DOI: 10.2991/ijcis.2018.25905182. 
IF: 2,153 Q: 3

Abouzid H, Chakkor O, Reyes OG, Ventura S; Signal speech reconstruction and noise removal using convolutional denoising audioencoders with neural deep learning. ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING. 2019. 100. 3. 501-512. DOI: 10.1007/s10470-019-01446-6. 
IF: 0,823 Q: 4

Maqsood R, Ceravolo P, Ventura S; Discovering Students' Engagement Behaviors in Confidence-based Assessment. PROCEEDINGS OF 2019 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON). 2019. 0. 841-846. DOI: 10.13140/RG.2.2.30967.68007.
     
Quintero-Dominguez LA, Morell C, Ventura S; WordificationMI: multi-relational data mining through multiple-instance propositionalization. PROGRESS IN ARTIFICIAL INTELLIGENCE. 2019. 8. 3. 375-387. DOI: 10.1007/s13748-019-00186-y. 

Members

Principal Investigator
Sebastián Ventura Soto

Researchers
Carlos García Martínez
Eva Lucrecia Gibaja Galindo
José María Luna Ariza
María Luque Rodríguez
Cristóbal Romero Morales
José Raúl Romero Salguero
Amelia Zafra Gómez

Post-Doctoral Researchers
José María Moyano Murillo
Aurora Ramírez Quesada

Pre-Doctoral Researchers
Rafael Barbudo Lunar
José Antonio Delgado Osuna
Aurora Esteban Toscano
María del Carmen Luque Guzmán
Antonio Rafael Moya Martín-Castaño
Eduardo Pérez Perdomo
Antonio Manuel Trasierras Fresco