K nearest neighbours with mutual information for simultaneous classification and missing data imputation

García-Laencina, Pedro J.; Sancho‐Gómez, José‐Luis; Figueiras-Vidal, Anı́bal R.; Verleysen, Michel

doi:10.1016/j.neucom.2008.11.026

Cited by 208 publications

(95 citation statements)

References 18 publications

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“…Mean and mode imputation (Mimpute) [12,13,14] consists of replacing the unknown/missing value for a given attribute by the mean (quantitative attribute) or mode (qualitative attribute) of all known/available values of that attribute. However, replacing all missing records with a single value distorts the input data distribution.…”

Section: Statistical Imputation Methodsmentioning

confidence: 99%

“…Hot deck imputation (HDimpute) [15] replaces the missing data with the values from the input vector that is closest in terms of the attributes that are known in both patterns. Unlike Mimpute, this method attempts to preserve the distribution by substituting different observed values for each missing item [12]. Another solution is provided by Cold Deck imputation (CDimpute) method, which is similar to hot deck but the data source must be other than the current data set.…”

Section: Statistical Imputation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Scalable Algorithms for Missing Value Imputation

Mohamed¹,

Hashem²,

Abdelsamea³

2014

IJCA

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Statistical Imputation Techniques have been proposed mainly with the aim of predicting the missing values in the incomplete sets as an essential step in any data analysis framework. K-means-based Imputation, as a representative statistical imputation method, has been producing satisfied results in terms of effectiveness and efficiency in handling popular and freely available data set (e.g., Bupa, Breast Cancer, Pima, etc.). The main idea of K-means based methods is to impute the missing value relying on the prototypes of the representative class and the similarity of the data. However, such kinds of methods share the same limitations of the Kmeans as data mining technique. In this paper and motivated by such drawbacks, we introduce simple and efficient imputation methods based on K-means to deal with the missing data from various classes of data sets. Our proposed methods give higher accuracy than the one given by the standard K-means.

show abstract

Section: Statistical Imputation Methodsmentioning

confidence: 99%

Section: Statistical Imputation Methodsmentioning

confidence: 99%

Scalable Algorithms for Missing Value Imputation

Mohamed¹,

Hashem²,

Abdelsamea³

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…The suggested method is based on the well-known k nearest-neighbors imputation procedure [11,25,26] that has been successfully demonstrated for missing data imputation in forestry remote sensing, where missing forest-related characteristics were imputed using the Landsat thematic mapper TMand ETM+ satellite image data [27][28][29][30][31] or airborne light detection and ranging LiDARimage data [32][33][34].…”

Section: Sensor-to-sensor Prediction (Sentos) Methodsmentioning

confidence: 99%

Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

et al. 2013

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Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor's missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.

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“…On the contrary, the Dudani measure is less used in SEE; however, it was proved to be efficiency in studies (García-Laencina et al, 2009;Pan et al, 2015). It was proposed to weigh evidence of a neighbor in KNN classification problems (Dudani, 1976).…”

Section: Adaptation Techniquementioning

confidence: 99%

Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study

Huang

Keung

Sarro

et al. 2017

Journal of Systems and Software

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Being able to predict software quality is essential, but also it pose significant challenges in software engineering. Historical software project datasets are often being utilized together with various machine learning algorithms for fault-proneness classification.Unfortunately, the missing values in datasets have negative impacts on the estimation accuracy and therefore, could lead to inconsistent results. As a method handling missing data, K nearest neighbor (KNN) imputation gradually gains acceptance in empirical studies by its exemplary performance and simplicity. To date, researchers still call for optimized parameter setting for KNN imputation to further improve its performance. In the work, we develop a novel incomplete-instance based KNN imputation technique, which utilizes a cross-validation scheme to optimize the parameters for each missing value. An experimental assessment is conducted on eight quality datasets under various missingness scenarios. The study also compared the proposed imputation approach with mean imputation and other three KNN imputation approaches. The results show that our proposed approach is superior to others in general. The relatively optimal fixed parameter settings for KNN imputation for software quality data is also 2 determined. It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation.

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K nearest neighbours with mutual information for simultaneous classification and missing data imputation

Cited by 208 publications

References 18 publications

Scalable Algorithms for Missing Value Imputation

Scalable Algorithms for Missing Value Imputation

Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study

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