Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems

Özşen, Seral; Güneş, Salih

doi:10.1016/j.eswa.2007.09.063

Cited by 59 publications

(16 citation statements)

References 12 publications

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“…Table 8 shows the overall results of the Liver Disorders data set. Similar to the previous experiment, only a single train-test ratio (9:1 or tenfold cross-validation) that was used in [51] and [53] was used for comparison purposes. The accuracy rates of LSSVM with fuzzy weighting model [53] and GA-AWAIS [51] were 94.29 and 85.21 %, respectively.…”

Section: Liver Disorders Data Setmentioning

confidence: 99%

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A hybrid FAM–CART model and its application to medical data classification

Seera

Lim

Tan

et al. 2015

Neural Comput & Applic

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In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability-plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM-CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM-CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stabilityplasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM-CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM-CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

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Section: Liver Disorders Data Setmentioning

confidence: 99%

“…Table 7 shows a comparison with other published results of the Heart Disease data set. In this experiment, a single train test ratio (9:1 or tenfold cross-validation) as that used in [51] and [52] was adopted. The Genetic algorithms AWAIS [51] model produced 87.43 % accuracy, while IBK [52] showed a perfect accuracy rate.…”

Section: Heart Disease Data Setmentioning

confidence: 99%

A hybrid FAM–CART model and its application to medical data classification

Seera

Lim

Tan

et al. 2015

Neural Comput & Applic

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“…There are many data pre-processing methods conducted in literature. Among these, Özşen et al proposed an attribute weighting method based on genetic algorithms for solving of heart disease and liver disorders problems and also suggested GA-AWAIS approach [1]. Gançarski et al proposed two new feature weighting methods on the basis of coevolutive algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

Polat

2011

J Med Syst

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In this paper, attribute weighting method based on the cluster centers with aim of increasing the discrimination between classes has been proposed and applied to nonlinear separable datasets including two medical datasets (mammographic mass dataset and bupa liver disorders dataset) and 2-D spiral dataset. The goals of this method are to gather the data points near to cluster center all together to transform from nonlinear separable datasets to linear separable dataset. As clustering algorithm, k-means clustering, fuzzy c-means clustering, and subtractive clustering have been used. The proposed attribute weighting methods are k-means clustering based attribute weighting (KMCBAW), fuzzy c-means clustering based attribute weighting (FCMCBAW), and subtractive clustering based attribute weighting (SCBAW) and used prior to classifier algorithms including C4.5 decision tree and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed method, the recall, precision value, true negative rate (TNR), G-mean1, G-mean2, f-measure, and classification accuracy have been used. The results have shown that the best attribute weighting method was the subtractive clustering based attribute weighting with respect to classification performance in the classification of three used datasets.

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“…It is well known that feature weighting is effective for pattern classification as shown in [47,48,49]. It is expected that the classification accuracy can be further improved by weighting the proper first PHOG-PCA features since some local regions are less relevant for vehicle detection than the others.…”

Section: Hypothesis Verification (Hv)mentioning

confidence: 99%