Hybrid Intelligent Systems for Data Mining and Applications

Hybrid Artificial Intelligent Systems (HAIS) are becoming a consolidated discipline that improves various existing approaches in a wide variety of application areas. Thus, HAIS fields real-world problems by applying multiple techniques, showing the strengths when solving particularly tough problems. Moreover, due to the complex nature of the problems, it is difficult to fairly compare their performance with classical approaches. In this sense, the aim of this special session is to present research conducted on intelligent systems and, in particular, approaches focused on solving data mining problems. Data mining techniques are relevant for scientific and technical research as they provide a set of tools to discover knowledge from experimental data. Thus, the scope of this session covers data mining aspects, including algorithms, systems, and applications. It also encourages authors to submit novel hybrid methodologies.

  1. Machine learning techniques and algorithms: Classification, clustering, feature selection, soft computing, ensembles
  2. Pattern recognition
  3. Data mining for complex data: Temporal data, spatial data, multimedia, streaming data
  4. Novel data mining algorithms
  5. Data mining applications: Biomedical, financial, industrial, web mining, social mining
  • Co-Chairs
  1. Alicia Troncoso Lora, Pablo de Olavide University of Seville, Spain
  2. Francisco Martínez Álvarez, Pablo de Olavide University of Seville, Spain
  3. María Martínez Ballesteros, University of Seville, Spain
  4. Jorge García Gutiérrez, University of Seville, Spain
  • Contact information
  • Francisco Martínez Álvarez
    E-mail: fmaralv@upo.es
  • Pablo de Olavide University of Seville (Spain)

  • Telephone number. +34 954 977370

Important Dates

Paper submission

These papers go through the same reviewing and selection process like those submitted regularly