Correlation based feature selection method

Michalak, Krzysztof; Kwaśnicka, Halina

doi:10.1504/ijbic.2010.036158

Cited by 40 publications

(13 citation statements)

References 11 publications

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“…Even with the increasing missing rates up to 50%, the proposed method performed better than SVM-FCM. This is because FPCA reduces the number of dataset dimensions compared to the full dataset [28], thus allowing FCM to perform better. Table 7, we can see that the average of errors between actual value and estimated value are minimal.…”

Section: Rmsementioning

confidence: 99%

Missing data imputation with fuzzy feature selection for diabetes dataset

Dzulkalnine

Sallehuddin

2019

SN Appl. Sci.

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Missing data in datasets remain as a difficulty in terms of data analysis in various research fields, especially in the medical field, as it affects the treatment and diagnosis that the patient should receive. In this research, Fuzzy c-means (FCM) are used to impute the missing data. However, like in most data imputation methods, FCM do not consider the presence of irrelevant features. Irrelevant features can increase the computational time of the imputation process and decrease the accuracy of the prediction. Feature selection techniques can alleviate this problem by selecting the most relevant features and reducing the dataset size. Fuzzy principal component analysis (FPCA) is used as the feature selection method in this study as it considers the presence of outliers compared to classical PCA as outliers are the main reason some features renders irrelevant. Therefore, an improved hybrid imputation model of FPCA-Support vector machines-FCM (FPCA-SVM-FCM) has been proposed and employed in this study. The efficiency of the proposed model is investigated on one dataset which is Pima Indians Diabetes dataset. Experimental results showed that the proposed hybrid imputation model is better than the existing methods by producing a more accurate estimation in terms of accuracy, RMSE and MAE. The proposed method was also validated by using Wilcoxon rank sum and Theil's U test and obtained good results compared to SVM-FCM. Therefore, it can be used as an alternative tool for handling missing data in order to obtain a better quality dataset.

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Section: Rmsementioning

confidence: 99%

Missing data imputation with fuzzy feature selection for diabetes dataset

Dzulkalnine

Sallehuddin

2019

SN Appl. Sci.

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“…The training data and misclassified points (shown in dashed lines) can be visualized on the parallel coordinates plot. These features can also be selected sequentially or by using Correlation Feature Selection Method based filtering [15] or by using predictors transformed by principal component analysis to design accurate yet lightweight classifiers.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of supervised classification algorithms for human activity recognition with inertial sensors

Zebin

Scully²,

Ozanyan

2017

2017 Ieee Sensors

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Abstract-We report first results on developing smart sensor systems for the automatic and frequent collection of animal weight and gait data, under the hostile conditions of a livestock farm. The novelty in our approach is to sense frequently the animals' floor contact, in suitably chosen locations, under natural and unobtrusive conditions. We demonstrate a pilot low profile rubberized mat sensor heads, delivering a large number of plastic optical fiber transmission measurements taken frequently from individual deformation sensors. The acquired data allows gait analysis from guided-path tomography images or by machine learning. We demonstrate weight estimation with accuracy better than 1% over a range suitable for deployment in pig farms.

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“…As a result, the size of the hypothesis space will be reduced, classifiers will operate faster and more effectively, and classification accuracy will improve. Algorithms that perform feature selection can generally be divided into two categories, namely the wrappers (Michalak and Kwasnicka 2010) and the filters (Kohavi and John 1997). The filters, unlike the wrappers, operate independent of any learning algorithm.…”

Section: Variable Selection In Datasetsmentioning

confidence: 99%

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

Hájek

2011

Cent Eur J Oper Res

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This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of natural language that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network is designed to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i.e. specific determinants are used for each industry). The results show that the credit rating classes assigned to bond issuers can be classified with high classification accuracy using low numbers of features, membership functions, and if-then rules. The comparison of selected fuzzy rule-based classifiers indicates that it is possible to increase classification performance by using different classifiers for individual industries.

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Correlation based feature selection method

Cited by 40 publications

References 11 publications

Missing data imputation with fuzzy feature selection for diabetes dataset

Missing data imputation with fuzzy feature selection for diabetes dataset

Evaluation of supervised classification algorithms for human activity recognition with inertial sensors

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

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