Novel Grouping Method-based support vector machine plus for structured data

Hou, Qiuling; Ling, Zhen; Deng, Ning; Jing, Ling

doi:10.1016/j.neucom.2016.03.086

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Kahramanli and Allahverdi (Kahramanli & Allahverdi, 2009) mentioned 96.78% using an artificial immune system-based approach; Mȩ_ zyk and Unold (Mȩ_ zyk & Unold, 2011) showed 93.87% using an artificial immune system with fuzzy partition learning; Zangooei et al (Zangooei, Habibi, & Alizadehsani, 2014) obtained 98.52% using support vector regression and a multiobjective evolutionary hybridization; Naik et al (Naik, Nayak, Behera, & Abraham, 2016) achieved 76.294% using a harmony search-based functional link higher order artificial neural network; Kulluk et al (Kulluk, Özbakir, Tapkan, & Baykaso glu, 2015) attained 93% using cost-sensitive meta-learning classifier; Hou et al (Hou, Zhen, Deng, & Jing, 2016) stated 91.31% using grouping method based support vector machine; Hayashi and Fukunaga (Hayashi & Fukunaga, 2016) networks; and Kanik (Kanik, 2012) achieved 94% using rough set approach. Experimental results showed that EL1-and KM-EL1-based diagnostic models have not shown significant performance.…”

Section: Resultsmentioning

confidence: 99%

“…Polat and Gunes (Polat & Güneş, 2006) stated 92.5% accuracy using FS‐AIRS with fuzzy res. ; Polat and Güneş (Polat & Güneş, 2007) declared 94.1% using FS‐fuzzy‐AIRS; E. Dogantekin et al (Dogantekin et al, 2009) obtained 94.1% using LDA‐ANFIS; Bascil and Temurtas (Bascil & Temurtas, 2011) attained 91.8% using MLNN (MLP) + LM; Tan et al (Tan, Teoh, Yu, & Goh, 2009) achieved 92.4% using CORE; Calisir and Dogantekin (Çalişir & Dogantekin, 2011) stated 95.0% using PCA‐LSSVM; Sartakhti et al (Sartakhti, Zangooei, & Mozafari, 2012) stated 96.2% using SVM‐SA; Kahramanli and Allahverdi (Kahramanli & Allahverdi, 2009) mentioned 96.78% using an artificial immune system‐based approach; Mȩżyk and Unold (Mȩżyk & Unold, 2011) showed 93.87 % using an artificial immune system with fuzzy partition learning; Zangooei et al (Zangooei, Habibi, & Alizadehsani, 2014) obtained 98.52% using support vector regression and a multiobjective evolutionary hybridization; Naik et al (Naik, Nayak, Behera, & Abraham, 2016) achieved 76.294% using a harmony search‐based functional link higher order artificial neural network; Kulluk et al (Kulluk, Özbakir, Tapkan, & Baykasoğlu, 2015) attained 93% using cost‐sensitive meta‐learning classifier; Hou et al (Hou, Zhen, Deng, & Jing, 2016) stated 91.31% using grouping method based support vector machine; Hayashi and Fukunaga (Hayashi & Fukunaga, 2016) mentioned 83.24% using recursive rule extraction algorithm; Aldape‐Perez et al (Aldape‐Perez, Yanez‐Marquez, Camacho‐Nieto, & J Arguelles‐Cruz, 2012) showed 85.16% using an associative memory‐based classifier; Ansari et al (Ansari, Shafi, & Ansari, 2011) obtained 92% using artificial neural networks; and Kanik (Kanik, 2012) achieved 94% using rough set approach. Experimental results showed that EL1‐ and KM‐EL1‐based diagnostic models have not shown significant performance.…”

Section: Resultsmentioning

confidence: 99%

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An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

et al. 2020

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In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k-means clustering and improved ensemble-driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f-measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k-means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree-based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.

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