scholarly journals A Constructive Fuzzy Representation Model for Heart Data Classification

2021 ◽  
Author(s):  
Michael D. Vasilakakis ◽  
Dimitris K. Iakovidis ◽  
George Koulaouzidis
Keyword(s):  
Heart Failure ◽  
Fuzzy Logic ◽  
Feature Selection ◽  
Data Classification ◽  
Classification Model ◽  
Mortality And Morbidity ◽  
Home Telemonitoring ◽  
Proposed Model ◽  
Respective Treatment ◽  
Fuzzy Representation

The early detection of Heart Disease (HD) and the prediction of Heart Failure (HF) via telemonitoring and can contribute to the reduction of patients’ mortality and morbidity as well as to the reduction of respective treatment costs. In this study we propose a novel classification model based on fuzzy logic applied in the context of HD detection and HF prediction. The proposed model considers that data can be represented by fuzzy phrases constructed from fuzzy words, which are fuzzy sets derived from data. Advantages of this approach include the robustness of data classification, as well as an intuitive way for feature selection. The accuracy of the proposed model is investigated on real home telemonitoring data and a publicly available dataset from UCI.

scholarly journals A Novel Model for Imbalanced Data Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6680-6687
Author(s):  
Jian Yin ◽  
Chunjing Gan ◽  
Kaiqi Zhao ◽  
Xuan Lin ◽  
Zhe Quan ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Public Datasets ◽  
Distribution Cost ◽  
Novel Model ◽  
Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

scholarly journals MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Surendran Rajendran ◽  
Osamah Ibrahim Khalaf ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Process ◽  
Data Classification ◽  
Deep Belief Network ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Belief Network ◽  
Big Data Classification

AbstractIn recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

scholarly journals BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems

2021 ◽  
Vol 11 (14) ◽  
pp. 6516
Author(s):  
Hamouda Chantar ◽  
Thaer Thaher ◽  
Hamza Turabieh ◽  
Majdi Mafarja ◽  
Alaa Sheta
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Data Classification ◽  
Classification Model ◽  
Model Complexity ◽  
Binary Particle Swarm Optimization ◽  
Time Varying ◽  
Classification Problems ◽  
Whale Optimization

Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.

scholarly journals Feature Selection for Colon Cancer Detection Using K-Means Clustering and Modified Harmony Search Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 570
Author(s):  
Jin Hee Bae ◽  
Minwoo Kim ◽  
J.S. Lim ◽  
Zong Woo Geem
Keyword(s):  
Colorectal Cancer ◽  
Feature Selection ◽  
Candidate Genes ◽  
Search Algorithm ◽  
Harmony Search ◽  
Feature Selection Method ◽  
Harmony Search Algorithm ◽  
Classification Model ◽  
Fisher Score ◽  
Proposed Model

This paper proposes a feature selection method that is effective in distinguishing colorectal cancer patients from normal individuals using K-means clustering and the modified harmony search algorithm. As the genetic cause of colorectal cancer originates from mutations in genes, it is important to classify the presence or absence of colorectal cancer through gene information. The proposed methodology consists of four steps. First, the original data are Z-normalized by data preprocessing. Candidate genes are then selected using the Fisher score. Next, one representative gene is selected from each cluster after candidate genes are clustered using K-means clustering. Finally, feature selection is carried out using the modified harmony search algorithm. The gene combination created by feature selection is then applied to the classification model and verified using 5-fold cross-validation. The proposed model obtained a classification accuracy of up to 94.36%. Furthermore, on comparing the proposed method with other methods, we prove that the proposed method performs well in classifying colorectal cancer. Moreover, we believe that the proposed model can be applied not only to colorectal cancer but also to other gene-related diseases.

scholarly journals FUZZY UNORDERED RULE USING GREEDY HILL CLIMBING FEATURE SELECTION METHOD: AN APPLICATION TO DIABETES CLASSIFICATION

2021 ◽  
Vol 20 (Number 3) ◽  
pp. 391-422
Author(s):  
Hayder Naser Khraibet Al-Behadili ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Classification Criteria ◽  
Hill Climbing ◽  
Classification Model ◽  
Support Vector ◽  
Input Variables ◽  
Fuzzy Logic Technique

Diabetes classification is one of the most crucial applications of healthcare diagnosis. Even though various studies have been conducted in this application, the classification problem remains challenging. Fuzzy logic techniques have recently obtained impressive achievements in different application domains especially medical diagnosis. Fuzzy logic technique is not able to deal with data of a large number of input variables in constructing a classification model. In this research, a fuzzy logic technique using greedy hill climbing feature selection methods was proposed for the classification of diabetes. A dataset of 520 patients from the Hospital of Sylhet in Bangladesh was used to train and evaluate the proposed classifier. Six classification criteria were considered to authenticate the results of the proposed classifier. Comparative analysis proved the effectiveness of the proposed classifier against Naive Bayes, support vector machine, K-nearest neighbour, decision tree, and multilayer perceptron neural network classifiers. Results of the proposed classifier demonstrated the potential of fuzzy logic in analyzing diabetes patterns in all classification criteria.

scholarly journals NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 5370-5375
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Big Data ◽  
Data Classification ◽  
Classification Model ◽  
Distributed Data ◽  
Data Mining Technique ◽  
Data Set ◽  
Internet Applications ◽  
Neuro Fuzzy

With the growing culture of Internet applications and their usage lead to challenging task for storing a massive volume of high-velocity data from different fields. This result an evolution of big data with integrated, i.e. Volume, Velocity, and Variety (3V's). The voluminous data extraction is a very complex task which is not possible form classical data mining techniques. Therefore, a big data mining technique is introducing by modifying traditional data mining scheme using a novel of Neuro-Fuzzy Logic based approach, i.e. named as NFDDC. The proposed distributed data classification model performs into three stages first- reduce the data set dimension, second- data clustering, and third-data classification using the neuro-fuzzy method. The performance of the NFDDC system is analysed using two different datasets, i.e. medical data and e-commerce datasets. Additionally, comparative analysis is performed by measuring the accuracy of existing CCSA algorithm with proposed NFDDC algorithm and will get 90% accuracy in data classification

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