Wyniki wyszukiwania - BazTech

1

A weighted wrapper approach to feature selection

Kusy Maciej, Zajdel Roman

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 4

685--696

EN

This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson’s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.

2

A hybrid gene selection method for microarray recognition

Shukla A. K., Singh P., Vardhan M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

975--991

EN

DNA microarray data is expected to be a great help in the development of efficient diagnosis and tumor classification. However, due to the small number of instances compared to a large number of genes, many of the computational learning methods encounter difficulties to select the low subgroups. In order to select significant genes from the high dimensional data for tumor classification, nowadays, several researchers are exploring microarray data using various gene selection methods. However, there is no agreement between existing gene selection techniques that produce the relevant gene subsets by which it improves the classification accuracy. This motivates us to invent a new hybrid gene selection method which helps to eliminate the misleading genes and classify a disease correctly in less computational time. The proposed method composes of two-stage, in the first stage, EGS method using multi-layer approach and f-score approach is applied to filter the noisy and redundant genes from the dataset. In the second stage, adaptive genetic algorithm (AGA) work as a wrapper to identify significant genes subsets from the reduced datasets produced by EGS that can contribute to detect cancer or tumor. AGA algorithm uses the support vector machine (SVM) and Naïve Bayes (NB) classifier as a fitness function to select the highly discriminating genes and to maximize the classification accuracy. The experimental results show that the proposed framework provides additional support to a significant reduction of cardinality and outperforms the state-of-art gene selection methods regarding accuracy and an optimal number of genes.

3

Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement

Jin J.-F., Yang Y.-C., Zou F.-Y.

Fibres & Textiles in Eastern Europe

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2016

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Vol. 24, no. 5 (119)

110--118

EN

The purpose of this paper was to develop an intelligent recognition system consisting of a feature reduction method combining cluster and correlation analyses, and a probabilistic neural network (PNN) classifier to identify different types of hip shape from 3D measurement for each person. Firstly 28 items reflecting lower body part information of 300 female university students aging from 20 to 24 years were selected. The feature reduction method was employed to extract typical indices. Secondly hip shapes were subdivided into five types by a K-means cluster and analysis of variance (ANOVA). Finally the PNN was then trained to serve as a classifier for identifying five different hip shape types. The average classification accuracy of the scheme proposed was 97.37%, and its effectiveness was successfully validated by comparing with the BP and Support Vector Machine (SVM) scheme. Thus an intelligent recognition system was developed to make hip shape type classification of high-precision and time saving.

PL

Model łączy analizę skupień i korelacji oraz probabilistyczną sztuczną sieć neuronową dla identyfikacji różnych typów kształtów bioder opartą o pomiary 3D poszczególnych osób. Wyselekcjonowano 28 przypadków odzwierciedlających dolną część sylwetki 300 studentek w wieku od 20 do 24 lat. Zastosowano metodę redukcji poszczególnych właściwości dla wybrania typowych wskaźników. Następnie kształt bioder podzielono na 5 typów za pomocą algorytmu klastrowego i systemu ANOVA (analiza wariancji). Następnie przeprowadzono trening sieci neuronowej aby mogła posłużyć jako klasyfikator identyfikacji 5 różnych kształtów bioder. Przeciętna dokładność klasyfikacji proponowanego systemu wynosiła 97,37%, a efektywność była sukcesywnie sprawdzana przez porównanie schematów BP i SVM. W ten sposób stworzono inteligentny system rozpoznania typu kształtu bioder o dużej precyzji, pozwalający na oszczędność czasu.