Feature selection using fuzzy entropy measures with similarity classifier

Luukka, Pasi

doi:10.1016/j.eswa.2010.09.133

Cited by 284 publications

(131 citation statements)

References 20 publications

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“…The same dataset has been used in the reference (Kayaer and Yildirim, 2003;Goncalves et al, 2006;Polat and Gunes, 2007;Kahramanli and Allahverdi, 2008;Temurtas et al, 2009;Dogantekin et al, 2010;Ganji and Abadeh, 2010;Jayalakshmi and Santhakumaran, 2010;Ephzibah, 2011;Ganji and Abadeh, 2011;Kala et al, 2011;Karegowda et al, 2011;Lee, 2011;Lukka, 2011;Orkcu and Bal, 2011;Qasem and Shamsuddin, 2011;Selvakuberan et al, 2011;Karatsiolis and Schizas, 2012;Aslam et al, 2013;Das et al, 2013;Kalaiselvi and Nasira, 2014;Seera and Lim, 2014;Choubey and Paul, 2016 …”

Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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Abstract:The modern society is prone to many life-threatening diseases, which if diagnosed early, can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes disease. There are already several existing methods, which have been implemented for the diagnosis of diabetes dataset. Here, the proposed approach consists of two stages: in first stage Genetic algorithm (GA) used as an attribute (feature) selection which reduces 4 attributes among 8 attributes, and in the second stage Radial Basis Function Neural Network (RBF NN) has been used for classification on selected attributes among all the attributes. The experimental results show the performance of the proposed methodology on Pima Indian Diabetes Dataset (PIDD) and provide better classification for diagnosis of diabetes patients on PIDD. GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can also be used for other kinds of medical diseases.

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Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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“…In this method features is selected by fuzzy entropy measure and also with the help of similarity classifier [11]. The main principle behind similarity classifier is to create an ideal vector Vj=(-Vj(S1),……,Vj(Fn)) which represent the class j.…”

Section: 2fuzzy Entropy Measure Feature Selection With Similarity mentioning

confidence: 99%

Survey on Semi Supervised Classification Methods and Feature Selection

Innocent¹,

Kurian

2013

IJRET

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“…This is a filter method for feature selection with pairwise constraints, which specifies whether a pair of data samples belong to the same class (must-link constraints) or different classes (cannot-link constraint). Also, Luukka [5] has proposed a filter technique based on fuzzy entropy measures and tested it together with similarity classifier to do the feature selection in high dimensional medical datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] all the features that having the higher entropy than the mean entropy are removed, but Jaganathan and Kuppuchamy [11] have stated that features with highest entropy values were the most informative ones. In this paper, we propose a new selection feature method based on two types of filter techniques, which are similarity and entropy measure.…”

Section: Introductionmentioning

confidence: 99%

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Sari¹,

Muchtar²,

Zarkasi³

et al. 2014

Jurnal Ilmu Komputer dan Informasi

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Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features. The experiment result shows that proposed method is able to deal with high dimensional data problem with mean accuracy of 80.5%. Keywords: classification, entropy, feature selection, high dimensional data, similarity AbstrakCurse of dimensionality merupakan masalah yang sering dihadapi pada proses klasifikasi. Transformasi fitur dan seleksi fitur sebagai metode dalam reduksi fitur bisa diterapkan untuk mengatasi masalah ini. Terlepas dari performanya yang baik, transformasi fitur sulit untuk diinterpretasikan karena ciri fisik dari fitur-fitur yang asli tidak dapat diperoleh kembali. Di sisi lain, seleksi fitur dengan proses komputasinya yang sederhana bisa mereduksi fitur-fitur yang tidak diperlukan dan mampu merepresentasikan data untuk memudahkan pemahaman terhadap data. Pada penelitian ini diajukan metode seleksi fitur baru yang berdasarkan pada dua pendekatan filter, yaitu similarity (kemiripan) dan entropi untuk mengatasi masalah data berdimensi tinggi. Tahap awal metode ini adalah menghitung nilai similarity antara fitur dengan vektor kelas dari 6 data berdimensi tinggi. Kemudian diperoleh nilai similarity maksimum yang digunakan untuk menghitung nilai entropi untuk setiap fitur. Fitur yang dipilih adalah fitur yang memiliki nilai entropi lebih tinggi daripada entropi rata-rata seluruh fitur. Fuzzy k-NN diterapkan untuk tahap klasifikasi data hasil seleksi fitur. Hasil percobaan menunjukkan bahwa metode yang diajukan mampu mengklasifikasi data berdimensi tinggi dengan rata-rata akurasi 80.5%.

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Feature selection using fuzzy entropy measures with similarity classifier

Cited by 284 publications

References 20 publications

GA_RBF NN: a classification system for diabetes

GA_RBF NN: a classification system for diabetes

Survey on Semi Supervised Classification Methods and Feature Selection

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