Incremental feature weighting for fuzzy feature selection

2019 ◽  
Vol 368 ◽  
pp. 1-19 ◽  
Author(s):  
Ling Wang ◽  
Jianyao Meng ◽  
Ruixia Huang ◽  
Hui Zhu ◽  
Kaixiang Peng
Keyword(s):  
Feature Selection ◽  
Feature Weighting ◽  
Fuzzy Feature Selection

scholarly journals Perbandingan Pembobotan Kriteria dan Seleksi Kriteria pada Pengelompokan Kinerja Karyawan dengan Fuzzy C-Means

2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Rian Sanjaya ◽  
Yessica Nataliani
Keyword(s):  
Information Technology ◽  
Feature Selection ◽  
Selection Criteria ◽  
Employee Performance ◽  
Essential Feature ◽  
Feature Weighting ◽  
Accuracy Rate ◽  
Work Quality ◽  
Feature Weights ◽  
Fuzzy C Means

Abstract.Comparison of Weighted Criteria and Selection Criteria for Employee Performance Grouping with Fuzzy C-Means. The development of information technology makes it easier for companies to do many things and affect company operations. One of the objects affecting the company development is employees. Employees’ performance can be observed from their discipline, honesty, cooperation, and work quality. The purpose of this study is to group the employees based on their performance using fuzzy c-means. There are two kinds of clustering explained in this paper, i.e., clustering with feature weighting and clustering with feature selection. Using the feature weights of 25%, 30%, 25%, and 20% for work discipline, honesty, cooperation, and work quality, respectively, the clustering with feature weighting gives an accuracy rate of 0.8462. While using feature selection, the fuzzy c-means give 1, where the work discipline and honesty are the critical features in clustering. Therefore, we find that honesty is the most essential feature to cluster the employees based on their performance from this research.Keywords: clustering, employees, fuzzy c-means, feature weighting, feature selectionAbstrak.Perkembangan teknologi informasi mempermudah perusahaan dalam melakukan banyak hal dan mempengaruhi operasional perusahaan. Salah satu objek yang mempengaruhi operasional perusahaan adalah kinerja karyawan. Penilaian kinerja karyawan didasarkan pada empat kriteria, yaitu kedisiplinan, kejujuran, kerja sama, dan kualitas kerja, Tujuan penelitian ini untuk melakukan pengelompokan karyawan dengan fuzzy c-means. Pengelompokan yang dilakukan dalam penelitian ini terdiri dari dua macam, yaitu pengelompokan dengan pembobotan kriteria dan pengelompokan dengan seleksi kriteria. Dengan bobot sebesar 25%, 30%, 25%, dan 20% untuk kriteria kedisiplinan, kejujuran, kerja sama, dan kualitas kerja, pengelompokan dengan pembobotan kriteria menghasilkan akurasi sebesar 0.8462. Pengelompokan FCM dengan seleksi kriteria menghasilkan kriteria kedisiplinan dan kejujuran merupakan dua kriteria yang penting dalam pengelompokan karyawan, dengan akurasi sebesar 1. Dari hasil perbandingan dua macam pengelompokan tersebut didapatkan bahwa kejujuran merupakan kriteria terpenting dalam pengelompokan karyawan berdasarkan kinerjanya.Kata Kunci: pengelompokan, karyawan, fuzzy c-means, pembobotan kriteria, seleksi kriteria

scholarly journals Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method

2021 ◽  
Vol 2129 (1) ◽  
pp. 012022
Author(s):  
Mohamad Faiz Dzulkalnine ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Nor Haizan Mohd Radzi ◽  
Noorfa Haszlinna Binti Mustaffa ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Cancer Dataset ◽  
Medical Practitioners ◽  
Fuzzy Feature Selection ◽  
Fuzzy Principal Component Analysis ◽  
Learning Analysis

Abstract In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.

scholarly journals Evolutionary Computation for Feature Manipulation in Salient Object Detection

2021 ◽  
Author(s):  
◽  
Shima Afzali Vahed Moghaddam
Keyword(s):  
Feature Selection ◽  
Feature Space ◽  
Salient Object Detection ◽  
Feature Weighting ◽  
Feature Combination ◽  
Salient Object ◽  
Combination Process ◽  
Input Feature ◽  
And Performance ◽  
High Level

<p>The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance.   Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation.   The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD.   This thesis proposes a feature weighting method utilizing PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods.  This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance.   This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain.   This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features.  This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set.</p>

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