Imputation And Classification Of Missing Data Using Least Square Support Vector Machines – A New Approach In Dementia Diagnosis

Abstract

Abstract-This paper presents a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database. The present study suggests a more robust technique that is likely to supply a value closer to the one that is missing for effective classification and diagnosis. Initially data is clustered and z-score method is used to select possible values of an instance with missing attribute values. Then multiple imputation method using LSSVM (Least Squares Support Vector Machine) is applied to select the most appropriate values for the missing attributes. Five imputed datasets have been used to demonstrate the performance of the proposed method. Experimental results show that our method outperforms conventional methods of multiple imputation and mean substitution. Moreover, the proposed method CZLSSVM (Clustered Z-score Least Square Support Vector Machine) has been evaluated in two classification problems for incomplete data. The efficacy of the imputation methods have been evaluated using LSSVM classifier. Experimental results indicate that accuracy of the classification is increases with CZLSSVM in the case of missing attribute value estimation. It is found that CZLSSVM outperforms other data imputation approaches like decision tree, rough sets and artificial neural networks, K-NN (KNearest Neighbour) and SVM. Further it is observed that CZLSSVM yields 95 per cent accuracy and prediction capability than other methods included and tested in the study.

Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

“…In general, missing data are either ignored in favor of simplicity or replaced with substituted values that are estimated using a statistical method, such as mean values. However, this method uses machine learning to predict missing values, because numerous studies have already proved that artificial intelligence is an effective and powerful tool for predicting the missing value [12]. Therefore, this investigation utilized a baseline and ensemble model with ANN, SVR, LR, and CART as artificial intelligence techniques, implemented in WEKA software, to predict 20 data points whose values are missing based on 40 other data points with values.…”

“…This predictive system can solve a multiple-output problem, since most of the predictions about a microbial community involve a single output, with physicochemical parameters as inputs, rather than DNA sequencing data as inputs, because DNA sequencing is expensive and time-consuming. The model enables researchers and environmental scientists who use AI for environmental purposes to predict future responses of microbial communities to various environmental scenarios [12].…”