A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

By Ibrahim Berkan Aydilek, Ahmet Arslan. Information Sciences, 2013

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6c7ca39ece117890c9f793af9d97536958d25f35

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Emilio Carrizosa
2. Dolores Romero Morales
3. Cynthia Rudin
4. Berk Ustun
5. Laura Palagi
6. Cristina Molero-Río
7. Amaya Nogales-Gómez
8. Belén Martín-Barragán

In Information Systems

1. Emilio Carrizosa

In Statistics and Probability

1. Mphago Banyatsang
2. Tlamelo Emmanuel
3. Emilio Carrizosa
4. Dimane Mpoeleng
5. Thabo Semong
6. Oteng Tabona
7. Thabiso M. Maupong

Citations in the corpus

• Time series interpolation via global optimization of moments fitting (2013, background): “The existence of missing values is a very common yet critical issue in data from a variety of fields such as engineering, physics, hydrology, finance, marketing or transportation; see [26,30,18,36,12,19,8,3].”
• A survey on missing data in machine learning (2021, methodology): “In some cases, hybrid approaches [11–15], have also been utilized to solve the weaknesses of the traditional imputation techniques.”