Sequential K-nearest Neighbor Research Articles

A large-scale MAGDM model based on SKNN and weighted clustering under incomplete information

Due to the complexity of multi-attribute group decision-making(MAGDM) problems, decision-makers frequently provide incomplete and hesitant evaluation information. How to make a valid decision based on incomplete and hesitant information is an important issue in MAGDM problems. Firstly, we novelly introduce a sequential K-nearest neighbor(SKNN) interpolation method to estimate the missing values based on the order of missing information proportions. When seeking the k-nearest neighbors of a decision-maker, an improved similarity measurement is proposed by considering the fuzzy-value similarity and the hesitation similarity between decision-makers simultaneously. Secondly, a K-means clustering algorithm based on attribute weighting is proposed to make the collective evaluation information more representative, and the attribute weights are obtained by constructing a mathematical model based on the minimum discrimination principle which can synthesize the subjective and objective weights information. Finally, an illustrative example and comparison analysis are demonstrated to show the validity and superiority of the proposed model.

A Study on Sequential K-Nearest Neighbor (SKNN) Imputation for Treating Missing Rainfall Data

A Study on Sequential K-Nearest Neighbor (SKNN) Imputation for Treating Missing Rainfall Data

순차 적응 최근접 이웃을 활용한 결측값 대치법

In this paper, we propose a Sequential Adaptive Nearest Neighbor(SANN) imputation method that combines the Adaptive Nearest Neighbor(ANN) method and the Sequential k-Nearest Neighbor(SKNN) method. When choosing the nearest neighbors of missing observations, the proposed SANN method takes the local feature of the missing observations into account as well as reutilizes the imputed observations in a sequential manner. By using a Monte Carlo study and a real data example, we demonstrate the characteristics of the SANN method and its potential performance.

Improved Statistical Methods are Needed to Advance Personalized Medicine

Common methods of statistical analysis, e.g. Analysis of Variance and Discriminant Analysis, are not necessarily optimal in selecting therapy for an individual patient. These methods rely on group differences to identify markers for disease or successful interventions and ignore sub-group differences when the number of sub-groups is large. In these circumstances, they provide the same advice to an individual as the average patient. Personalized medicine needs new statistical methods that allow treatment efficacy to be tailored to a specific patient, based on a large number of patient characteristics. One such approach is the sequential k-nearest neighbor analysis (patients-like-me algorithm). In this approach, the k most similar patients are examined sequentially until a statistically significant conclusion about the efficacy of treatment for the patient-at-hand can be arrived at. For some patients, the algorithm stops before the entire set of data is examined and provides beneficial advice that may contradict recommendations made to the average patient. Many problems remain in creating statistical tools that can help individual patients but this is an important area in which progress in statistical thinking is helpful.

Reuse of imputed data in microarray analysis increases imputation efficiency

BackgroundThe imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked.ResultsWe developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments.Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets.ConclusionsSequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data.