University Of California , Irvine Research Articles

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of this, we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process. For the selection of the best subset of features, we recommend using the binary ASSOA search strategy we developed before. According to the suggested approach, the number of features picked is reduced while maximizing classification accuracy. A ten-feature dataset from the University of California, Irvine (UCI) repository was used to test the proposed method’s performance vs. eleven other state-of-the-art approaches, including binary grey wolf optimization (bGWO), binary hybrid grey wolf and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hybrid GWO and genetic algorithm (bGWO-GA), binary firefly algorithm (bFA), and bGA methods. Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

Effective hybrid feature subset selection for multilevel datasets using decision tree classifiers

Feature selection is one of the most significant procedures in machine learning algorithms. It is particularly to improve the performance and prediction accuracy for complex data classification. This paper discusses a hybrid feature selection technique with the decision tree-based classification algorithm. The feature selected using information gain (IG) is combined with the features selected from ReliefF which generates the resultant feature subset. Then the resultant feature subset is in turn combined with a correlation-based feature selection (CFS) method to generate the aggregated feature subset. To perform classification accuracy on the aggregated feature subset, different decision trees-based classification algorithm such as C4.5, decision stumps, naive Bayes tree, and random forest with ten-fold cross-validation have been deployed. To check the prediction accuracy of the proposed work eight different multilevel University of California, Irvine (UCI) machine learning datasets have been used with minimum to maximum numbers of features. The main objective of the hybrid feature selection is to improve the classification accuracy, prediction and to reduce the execution time using standard datasets.

Improved Whale Optimization with Local-Search Method for燜eature燬election

Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm. In addition, disruptive selection (DS) is employed to select the solutions from the population for local search. DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions. Fifteen (15) standard benchmark datasets provided by the University of California Irvine (UCI) repository were used in evaluating the proposed approaches’ performance. Next, a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature. The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods. Hence, the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

A Data Classifier Based on Maximum Likelihood Evidential Reasoning Rule

In Dempster–Shafer evidence theory (DST), some classical evidence combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as to increase the accuracy of multiattribute classification decision making. However, most of them have not yet considered the interdependence among multiple pieces of evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous interdependence by introducing correlation factors into evidence combination. Hence, this paper designs a MAKER‐based classifier to mine more correlation information for data classification. Finally, some numerical analysis (classification) experiments are carried out using five popular benchmark databases from the University of California, Irvine (UCI) to illustrate that the refined measure for evidence interdependence can aggregate the fused probability (belief degree) into real class label of a sample and further improve classification accuracy.

Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is evaluated with parametric metric measures of sensitivity, specificity, and accuracy. In this work, the thyroid disease dataset collected from the machine learning University of California Irvine (UCI) database was used. The accuracy rate for the Differential Evolutionary algorithm got 0.884, the Butterfly optimization algorithm got 0.906, Fuzzy C-Means algorithm got 0.899 and DEBOA + Focused Concept Miner (FCM) proposed work 0.943.

Fusing Supervised and Unsupervised Measures for Attribute Reduction

It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which are insufficient to identify attributes with superior learning performance, such as stability and accuracy. In order to improve the classification stability and classification accuracy of reduct, in this paper, a novel measure is proposed based on the fusion of supervised and unsupervised perspectives: (1) in terms of supervised perspective, approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels; (2) in terms of unsupervised perspective, conditional entropy is helpful in quantitatively describing the internal structure of data itself. In order to prove the effectiveness of the proposed measure, 18 University of CaliforniaIrvine (UCI) datasets and 2 Yale face datasets have been employed in the comparative experiments. Finally, the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.

Big data analysis using a parallel ensemble clustering architecture and an unsupervised feature selection approach

Ensemble clustering is known as a challenging research direction in data mining. The results of several individual clustering methods are combined to produce higher quality final clusters. This study introduces a parallel hierarchical clustering approach based on the divide-and-conquer strategy, which is an attempt to realize faster and more efficient ensemble clustering. Here, we propose a cluster consensus selection approach that selects a subset of meriting primary clusters to participate in the final consensus. Considering the sample-cluster and cluster–cluster similarity on the selected primary clusters, we form the final clusters based on the clusters clustering technique as a consensus function. In addition, the proposed scheme is equipped with an unsupervised feature selection approach to remove features that do not contribute significantly to clustering. Extensive evaluations have been performed on datasets of different dimensions from the University of California Irvine (UCI) machine learning repository. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.

Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results; similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing.

Sparse Pinball Twin Bounded Support Vector Clustering

Analyzing unlabeled data is of prime importance in machine learning. Creating groups and identifying an underlying clustering principle is essential to many fields, such as biomedical analysis and market research. Novel unsupervised machine learning algorithms, also called clustering algorithms, are developed and utilized for this task. Inspired by twin support vector machine (TWSVM) principles, a recently introduced plane-based clustering algorithm, the twin bounded support vector clustering (TBSVC), is used in widespread clustering problems. However, TBSVC is sensitive to noise and suffers from low resampling stability due to usage of hinge loss. Pinball loss is another type of loss function that is less sensitive toward noise in the datasets and is more stable for resampling of datasets. However, the use of pinball loss negatively affects the sparsity of the solution of the problem. In this article, we present a novel plane-based clustering method, the sparse TBSVC using pinball loss (pinSTBSVC). The proposed pinSTBSVC is the sparse version of our recently proposed TBSVC using pinball loss (pinTBSVC). Sparse solutions help create better-generalized solutions to clustering problems; hence, we attempt to use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -insensitive pinball loss function to propose pinSTBSVC. The loss function used to propose pinSTBSVC provides sparsity to the solution of the problem and improves the aforementioned plane-based clustering algorithms. Experimental results performed on benchmark University of California, Irvine (UCI) datasets indicate that the proposed method outperforms other existing plane-based clustering algorithms. Additionally, we also give the application of our method in biomedical image clustering and marketing science. We show that the proposed method is more accurate on real-world datasets too. The code for the proposed algorithm is also provided on the author’s Github page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mtanveer1</uri> .

The UCI EyeMobile Preschool Vision Screening Program: Refractive Error and Amblyopia Results from the 2019-2020 School Year.

To introduce the University of California Irvine (UCI) EyeMobile for Children preschool vision screening program and describe the ophthalmic examination results of children who failed screening with the PlusoptiX S12C photoscreener during one school year. Children aged 30-72 months were screened with the PlusoptiX using ROC mode 3 during the 2019-2020 school year. Children who failed screening were referred for comprehensive eye examination on the EyeMobile mobile clinic. Presence of amblyopia risk factors (ARFs), amblyopia, and refractive error was determined via retrospective review of records. Amblyopia was defined as unilateral if there was ≥ 2-line interocular difference in the best-corrected visual acuity (BCVA) and as bilateral if BCVA was < 20/50 for children < 4 years old and < 20/40 for children ≥ 4 years old. ARFs were defined using 2021 American Association for Pediatric Ophthalmology and Strabismus (AAPOS) instrument-based screening guidelines. 5226 children were screened during the study period. Of the 546 children who failed screening, 350 (64%) obtained consent and were examined. Mean age of examined children was 4.45 years. Amblyopia was found in 8% of examined children, with unilateral amblyopia seen in 79% of amblyopic subjects. Glasses were prescribed to 246 (70.3%) children. Of the 240 children who received cycloplegic examinations, 43% had hyperopia and 30% had myopia. The positive predictive value (PPV) of the PlusoptiX screening for ARFs in children who received cycloplegic examinations was 70.4%. A significant proportion of Orange County preschoolers with refractive errors and amblyopia have unmet refractive correction needs. The PlusoptiX S12C photoscreener is an adequate screening device for the UCI EyeMobile for Children program, although modification of device referral criteria may lead to increased PPV. Further research is necessary to understand and overcome the barriers to childhood vision care in our community.

A New Rough Set Classifier for Numerical Data Based on Reflexive and Antisymmetric Relations

The grade-added rough set (GRS) approach is an extension of the rough set theory proposed by Pawlak to deal with numerical data. However, the GRS has problems with overtraining, unclassified and unnatural results. In this study, we propose a new approach called the directional neighborhood rough set (DNRS) approach to solve the problems of the GRS. The information granules in the DNRS are based on reflexive and antisymmetric relations. Following these relations, new lower and upper approximations are defined. Based on these definitions, we developed a classifier with a three-step algorithm, including DN-lower approximation classification, DN-upper approximation classification, and exceptional processing. Three experiments were conducted using the University of California Irvine (UCI)’s machine learning dataset to demonstrate the effect of each step in the DNRS model, overcoming the problems of the GRS, and achieving more accurate classifiers. The results showed that when the number of dimensions is reduced and both the lower and upper approximation algorithms are used, the DNRS model is more efficient than when the number of dimensions is large. Additionally, it was shown that the DNRS solves the problems of the GRS and the DNRS model is as accurate as existing classifiers.

Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

The vital signs can vary significantly depending on the daily physical activities, which may not be due to defects of the organs. Under remote human health-monitoring applications, for reliable disease diagnosis, recorded biomedical signals such as an electrocardiogram (ECG) and a photoplethysmogram (PPG) must be indexed with physical activity information, as it is unknown to the physicians and also to the computer-aided diagnostic system. Since deep-learning (DL) networks were explored for various vital signs extraction and disease classification, we present an effective convolutional neural network (CNN)-based human activity recognition (HAR) method by exploring suitable hyperparameters. CNN-based methods are evaluated using the acceleration signals taken from the standard HAR benchmark database, University of California, Irvine (UCI) with accuracy (ACC), model size (in kB), and processing time (PT), and also implemented on the Raspberry Pi 4 (R-Pi-4) to study real-time feasibility. Evaluation results showed that higher HAR accuracy can be achieved with the activation function of the exponential linear unit (ELU) for the 2-layer CNN with a segment size of 2.5 s (ACC of 89.05% and PT of 0.142 ms), the 4-layer CNN with a segment size of 1 s (ACC of 91.66% and PT of 0.541 ms), and the 6-layer CNN with a segment size of 2 s (ACC of 91.18% and PT of 1.672 ms). Results demonstrated that the selection of an optimal number of layers and hyperparameters plays a major role in achieving higher accuracy with lower computational time on both personal computer central processing unit (PC-CPU) and R-Pi computing platforms, which were not addressed in past studies.

Chi2-MI: A hybrid feature selection based machine learning approach in diagnosis of chronic kidney disease

• Development of an intelligent diagnosis system to detect chronic kidney disease. • A hybrid wrapper feature selection method (Chi 2 -MI) has been proposed and applied. • Data pre-processing methods are adopted to prepare the dataset for the model. • Most impactful features based on correlation scores are selected to predict the CKD. • Extra tress classifier can diagnose CKD with 98% accuracy among 14 learning models. Early detection and characterization are considered crucial in treating and controlling the chronic renal disease. Because of the rising number of patients, the high risk of progression to end-stage renal disease, and the poor prognosis of morbidity and mortality, chronic kidney disease (CKD) is a significant burden on the healthcare system. Detecting CKD in its early stages is critical for saving millions of lives. The uniqueness of this study lies in developing a diagnosis system to detect chronic kidney disease using different Machine Learning (ML) algorithms with the support of a hybrid feature selection approach. This study exploited the 400 clinical data of CKD patients based on the dataset supplied by the University of California Irvine (UCI) available at their Machine Learning repository. Different data preparation techniques like encoding categorical features, missing values imputation, removing outlier factors, handling data imbalance, scaling data at the same level, and selecting relevant features are adopted to prepare the dataset for the prediction model. A hybrid Chi-squared test (Chi2) and Mutual Information (MI) based feature selection approach is proposed to remove redundant features, and a Pearson correlation matrix is also computed to consider the top important features for the prediction. Lastly, the Extra tress classifier can diagnose CKD with 98% accuracy and a 2% true negative rate without data leakage out of 14 machine learning models. On the other hand, the Bagging classifier performed worst with only 60% accuracy.

C-Loss Based Higher Order Fuzzy Inference Systems for Identifying DNA N4-Methylcytosine Sites

DNA methylation is an epigenetic marker that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to improve the efficiency of methylation detection. This study proposes a fuzzy model with correntropy induced loss (C-loss) function to identify DNA N4-methylcytosine (4 mC) sites. To improve the robustness and performance of the model, we use kernel method and the C-loss function to build a higher order fuzzy inference systems. To test performance, our model is implemented on six 4 mC and eight University of California Irvine (UCI) datasets. The experimental results show that our model achieves better prediction performance.

BIO-INSPIRED ENSEMBLE FEATURE SELECTION (BIEFS) AND ENSEMBLE MULTIPLE DEEP LEARNING (EMDL) CLASSIFIER FOR BREAST CANCER DIAGNOSIS

Breast cancer is a major disease diagnosed in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC). Ensemble Multiple Deep Learning (EMDL) classifier is introduced which combines several individual models such as AdaBoost-Convolutional Neural Network (A-CNN), Long Short-Term Memory Network (LSTM), and Deep Auto-Encoder (DAE) is introduced to obtain better generalization performance. Then, these three classifiers such as A-CNN, LSTM, and DAE are used for ensemble classification using a Weight Majority Voting (WMV) mechanism. Wisconsin Diagnosis Breast Cancer (WOBC), and Wisconsin Diagnosis Breast Cancer (WDBC) are collected from University of California, Irvine (UCI) repository for experimentation. Evaluation metrics like Precision, Recall, F-measure, Accuracy, and Area Under Curve (AUC) are used to compare the results of proposed system and existing classifiers. All experiments are executed within a simulation environment and conducted in MATrix LABoratory R 2014 a (MATLAB 2014a) tool. The proposed EMDL classifier is showing better performance as compared to the traditional classification models.

Introduction to People in Control [People in Control

In this issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Control Systems</i> , we speak with Solmaz Kia, an associate professor of mechanical and aerospace engineering at the University of California, Irvine (UCI) and Tiago Roux Oliveira, an associate professor at the State University of Rio de Janeiro (UERJ), Brazil.

An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods

&lt;span lang="EN-US"&gt;Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods.&lt;/span&gt;

Sequential feature selection for heart disease detection using random forest

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential feature selection approach plays significant role in improving the performance of the proposed model. The proposed feature selection approach is evaluated using real world clinical heart disease dataset collected from University of California Irvine (UCI) data repository. Empirical test on validation set reveals that the proposed model performs well as compared to the existing methods. Overall, the state of-the-art heart disease detection model with classification accuracy of 98.53% is proposed for heart disease detection using SFS and random forest model.

Improving The Accuracy Of The C4.5 Algorithm Using Particle Swarm Optimization To Predicate Diabetes Patients

Diabetes is a disease that occurs due to an increase in blood sugar levels. Increased sugar levels can trigger death because they can cause damage to blood vessels, nerves, and other internal structures. To avoid bad consequences, it is important to predict whether a person has diabetes or not based on the information that each patient has. The method that can be used to predict whether someone has diabetes or not is a classification method. The C4.5 algorithm is one of the classification algorithms that can be used to predict diabetics. Although the C4.5 algorithm can be used to predict, getting a high accuracy value is also an important measurement. One thing that can be done to increase the accuracy value is to select the attribute that most influences a person to have diabetes or not. Feature selection greatly affects the accuracy of the C4.5 algorithm, particle swarm optimization is used as a feature selection method in this study. The purpose of this study is to increase the value of accuracy to predict whether someone has diabetes or not through feature selection using particle swarm optimization. The dataset used is the Pima Indian Diabetes Databases (PIDD) from the University of California Irvine (UCI) Machine Learning Repository. The results of the study show that feature selection using particle swarm optimization before using the C4.5 algorithm can increase accuracy by 6% from the previous accuracy value of 75%. The selected features are 4 attributes out of 9 attributes. The four attributes are Glucose, SkinThickness, BMI, and DiabetesPedigreeFunction. The results of feature selection using particle swarm optimization can increase the accuracy of predictions of whether someone suffering from diabetes or not.