Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Abstract

Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time.

A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records

“…Deep learning strategies, such as Datawig [18], can predict significantly more precise outcomes than classic data imputation approaches [19] by using the capabilities of GPU and huge data. However, as asserted in the statistical literature [20,21], as the volume of missing data increases, the fluctuation of impact forecasts increases and outcomes may not be accurate enough for hypothesis affirmation if over 40% of values are missing in relevant characteristics [11], implying that data imputation is not a good option when a considerable volume of data is missing. In addition, missing data in the healthcare domain does not happen randomly.…”