Associative classification of mammograms using weighted rules

Dua, Sumeet; Singh, Harpreet; Thompson, Hilary W.

doi:10.1016/j.eswa.2008.12.050

Cited by 65 publications

(37 citation statements)

References 8 publications

(21 reference statements)

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“… Tidak semua artribut digunakan dalam penelitian ini sehingga hanya atribut NIK, status KK (dalam data asli adalah kolom stkel), 11 aspek indikator kemiskinan dan evaluasi yang dipilih dari database keluarga miskin dan pemberdayaan. Sebelas aspek indikator kemiskinan tersebut adalah, aspek pangan (1), aspek sandang (2), aspek papan (3), aspek penghasilan (4), aspek kesehatan (5), aspek pendidikan (6), aspek kekayaan harta (7), aspek kepemilikan rumah (8), aspek air bersih (9), aspek listrik (10), dan aspek jumlah jiwa/ tanggungan (11) Data hasil integrasi dan reduksi sebanyak 831 record.…”

Section: Reduksi Dataunclassified

“…(1) WIT-FWIs() (2) [ ]={I I : ws(i) ≥ minws} (3) (6) FWIs_EXTEND([P]) (7) For all Li [P] do (8) Add (Li, ws(Li)) to FWIs (9) [Pi]= (10) For all Lj [P], with j > i do (11) X = Li Lj (12) Y = t(Li) ⋂ t(Lj) (13) If |t(Li)| = |Y| then ws(X)=ws(Li) (14) Else If |t(Lj)| = |Y| then ws(X)=ws(Lj) (15) Else ws(X) = COMPUTE-WS(Y) (16) if ws(X) ≥ minws then (17) …”

Section: Struktur Data Wit-tree (Weighted Itemsets Tidset-tree)mentioning

confidence: 99%

“…Beberapa penelitian menerapkan association rules mining (ARM) untuk membuat sistem klasifikasi. Hasilnya, metode baru yang disebut Association Rule Mining Classifier(ARMC) mampu meningkatkan akurasi klasifikasi [4][5] [6][6] [7] [8].…”

unclassified

“…Permasalahan ARMC tradisional adalah menganggap semua item mempunyai bobot kepentingan yang sama, hanya ditentukan ada atau tidak ada item tersebut dalam database [8].…”

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Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin

Khomsah¹,

Winarko²

2017

Indonesian J. Comput. Cybern. Syst.

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Section: Reduksi Dataunclassified

Section: Struktur Data Wit-tree (Weighted Itemsets Tidset-tree)mentioning

confidence: 99%

unclassified

“…Permasalahan ARMC tradisional adalah menganggap semua item mempunyai bobot kepentingan yang sama, hanya ditentukan ada atau tidak ada item tersebut dalam database [8].…”

unclassified

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Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin

Khomsah¹,

Winarko²

2017

Indonesian J. Comput. Cybern. Syst.

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“…A composition of association rules along with a rough set theory is used for computer aided diagnosis of mammogram images by Yun et al [14]. In [15], authors conferred a classification method, build on weighted association rules. In this paper, authors propose an algorithm to obtain significant and frequent patterns from mammogram dataset which are used for mammogram classification.…”

Section: Introductionmentioning

confidence: 99%

GLCM based Improved Mammogram Classification using Associative Classifier

Deshmukh¹,

Bhosle²

2017

IJIGSP

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Abstract-Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported [1]. Nowadays early detection of breast cancer became very important. Mammogram -a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations.The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.

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Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Suresh

Rao

Reddy

2019

Concurrency and Computation

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Summary Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.

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Associative classification of mammograms using weighted rules

Cited by 65 publications

References 8 publications

Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin

Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin

GLCM based Improved Mammogram Classification using Associative Classifier

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

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