Feature Selection through Biclustering to Identify Specific Language Impairment

  • Marta D. M. Noronha Pontifícia Universidade Católica de Minas Gerais
  • Luis E. Zárate Pontifícia Universidade Católica de Minas Gerais


Failure to express yourself verbally is a condition that affects nearly 7% of children worldwide, known as specific language impairment. The diagnosis is complex, involving specialists such as speech therapists and pediatricians. The dataset used in this work has many attributes and imbalanced data, which can harm knowledge discovery. We used biclustering to identify clusters that characterize children with speech problems and those with typical development. We propose selecting attributes through the significance analysis of biclusters, which enhanced the F-score and accuracy in models generated by using 90% of instances from the training dataset, compared to results from the original data.

Palavras-chave: Biclustering, Classification, Data mining, Speech signal processing, Specific language impairment


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NORONHA, Marta D. M.; ZÁRATE, Luis E.. Feature Selection through Biclustering to Identify Specific Language Impairment. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 121-128. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2023.232858.