Imputation of Missing Values Affecting the Software Performance of Component-based Robots

A hybrid machine learning system to impute and classify a component-based robot

“…For the selection of clustering techniques based on [1], only k-means and clustering techniques were used, although as detailed in the future work, the inclusion of hierarchical and density-based methods could be interesting. Secondly, for imputation techniques in [2] was concluded that, in general, the regression techniques that worked best were those used in the present investigation. Finally, for balancing techniques in the research carried out [3], all the techniques presented here were used with the exception of Borderline-SMOTE (BLSMOTE), whose adhesion is due to the fact that it is derived from SMOTE, a technique that stood out from the rest and could be of interest.…”

Bulletin of the Polish Academy of Sciences: Technical Sciences

“…For example, under-and over-sampling for imbalanced classes either decreases or increases numbers of instances in order to reach balance [21]. Missing or absent feature values can be replaced by averages or most common values [22]. Irregularities can also be studied in the context of data processing methods, such as discretisation.…”

The Best K-Exponential Moving Average with Missing Values: Gold Prices in Indonesia, Saudi Arabia, and Turkey during COVID-19

“…In terms of investment, complete data makes it easy for investors to make decisions quickly. Research related to missing values has been done a lot including for clustering [8], multivariate time series [25], active learning [9], a novel weighted distance threshold method [5], electronic health records [23], air pollution [18], financial statement fraud [6], software performance of components [4], mining gradual patterns [19], mail survey [16], spatial [3], obstetrics clinical data [2], autoencoder [12], transfer learning [14], encoder signals [26], Bayesian network [24], industry [22], etc.…”