UCI Machine Learning Repository Datasets Research Articles

Bluefin Trevally Optimizer (BTO): A Metaheuristic Algorithm Using Fuzzy Logic Controller for Feature Selection Problem

Metaheuristic algorithms are increasingly used for feature selection (FS) problems due to their robustness and searchability. This study presents a unique optimization technique called fuzzy bluefin trevally optimizer (BTO). BTO draws its inspiration from bluefin’s cooperative nature and sudden ambush behavior. The trevally will jump out of the water and attack its food in the air or even snag it off the water when it is sufficiently close to it. This study uses mathematical modeling to create an optimization algorithm to replicate dynamic patterns and behaviors. Since parameters play a critical role in optimization, we use the fuzzy concept for dynamic parameter adaptation in fuzzy BTO. By introducing the idea of signature and demonstrating that fuzzy BTO converges to the global optimal point, the mathematical basis for the proposed algorithm is provided. The convergence of fuzzy BTO is proved using the Markov chain property. On benchmark functions (BF), the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then the proposed algorithms are applied to a fuzzy FS problem. Then they are compared with traditional and recent metaheuristic algorithms on the UCI machine learning repository datasets. Finally, the statistical significance is examined using the Kruskal–Wallis test (KWT).

Outlier detection for partially labeled categorical data based on conditional information entropy

Labeling a large amount of data is exceptionally costly and practically infeasible, and thus available data may have missing labels. In this article, we investigate outlier detection for partially labeled categorical data based on conditional information entropy. Firstly, the equivalence class in a partially labeled categorical decision information system (p-CDIS) is introduced, so that the missing labels can be predicted by use of conditional probability. Then, conditional information entropy in a p-CDIS is calculated, which provides a more comprehensive measure of uncertainty. Additionally, the relative information entropy and relative cardinality in a p-CDIS are proposed. Next, the degree of outlierness and the weight function are presented to find outlier factors. Finally, an outlier detection method in a p-CDIS based on conditional information entropy is proposed, and a corresponding conditional information entropy algorithm (CEOF) is designed. To evaluate the stability of the CEOF algorithm, experiments are performed on ten UCI Machine Learning Repository datasets. Compared with five other algorithms, the proposed method is shown to have good effectiveness and adaptability for categorical data.

Hierarchical multi-scale parametric optimization of deep neural networks

AbstractTraditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. Graphical Abstract

Prediction of Heart Disease using Machine Learning Algorithms with Feature Selection Techniques

Data is an asset in the digital era, and enormous data was generating day by day in all the fields, including the healthcare industry. The data on the healthcare industry data consists of personal information and disease-related information about a patient and stored in various formats and units. Machine learning and Artificial Intelligence techniques will help us analyze the voluminous amount of data to identify the hidden patterns of a specific disease from the healthcare data and help us predict a particular disease in the future. In this paper, we proposed a decision support system to predict heart disease, especially cardiovascular disease, through machine learning algorithms. This system experimented with the reduced set feature of the UCI Machine learning repository dataset using a linear kernel-based support vector machine algorithm. This system has also compared it with other machine learning algorithms such as K-Nearest Neighbours, Decision tree, and Random forest in Python. All four machine learning algorithms' performance has been evaluated based on accuracy, misclassification rate, precision, recall, and f-score value. From the experimental results, SVM with a linear kernel function classification algorithm produces better accuracy of 95.08% compared with others for predicting heart disease.

Comparison of Machine Learning Techniques in the Classification of Parkinson’s Desease Sufferers

Parkinson's disease is a progressive and relatively common neurodegenerative disorder in the central nervous system where sufferers can have difficulty moving. This disease has a high mortality rate in the world of around 9.3 million in 2021. Meanwhile, in Indonesia, it is estimated that as many as 12,980 people die every year due to Parkinson's cases. This increase in cases of death is due to the lack of information about the initial symptoms and dangers of the disease, besides it is important to know how to prevent it early. Early detection of Parkinson's disease can prevent symptoms of a certain age thereby increasing life expectancy. The existence of a computer-based system for diagnosing Parkinson's disease is called a classification system where the system applies the Machine Learning method. This study aims to compare the performance of algorithms in the classification system of people with computer diseases. In this study, it used methods in Machine Learning such as K-NN, Multi Layer Percepteron (MLP), Linear Regression and Support Vector Machine (SVM). The data set in this study was obtained using the Weka application. The dataset used was Parkinson's Disease data totaling 195 rows of data taken from the UCI Machine Learning Repository Datasets. The results of the experiment based on the four algorithms showed that the poor performance was the Multi Layer Percepteron approach to regression data with an RSME value of 0.459. Meanwhile, the k-Neural Network Algorithm is a good classification technique forParkinson's problem with an RMSE value of 0.1895.

Application of Artificial Neural Network Algorithms to Heart Disease Prediction Models with Python Programming

Heart disease is one of the deadliest diseases in and is the number one killer in the world so many studies are carried out to contribute to predicting a person's heart disease. This study aims to help create an early heart disease prediction model from the UCI Machine Learning Repository dataset. The method proposed in this study is a deep learning technique that applies an artificial neural network algorithm with a hidden layer technique in making a heart disease prediction model. This research stage found problems in improving the accuracy of the datasets used by dealing with problems in pre-processing data, such as missing data and determining the form of data correlation. The model was then tested through a heart disease dataset and yielded 90% accuracy. With the creation of this prediction model with python programming, it is hoped that in addition to helping to make disease predictions, it can also provide further innovations in data science in the health sector.

Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems.

Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

Classification of COVID-19 by using supervised optimized machine learning technique

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.

APPLICATION AND ATTRIBUTE ANALYSIS IN THE MODEL OF CLASSIFYING HEART DISEASE

The heart is the central center in the human circulatory system. A malfunction of the heart that is not functioning is a condition in which the heart cannot carry out its duties properly. Selection of features that can reduce a very large dataset and in a data set that is not suitable can use a reduction model. The classification process is strongly influenced by an attribute. Various types of inappropriate redundancy have a negative effect on classification accuracy. Heart disease data was taken from the UCI Machine Learning Repository dataset. In this study, the researchers used the K-Nearest Neighbor (KNN) algorithm where the K-Nearest Neighbor algorithm can classify the results of heart disease accurately. The results are as follows 1.67358 rank one 1.33949 rank two, 1.27260 rank three, 1.2528 rank four, 1.24193 rank last

A Novel Filter-Wrapper Algorithm on Intuitionistic Fuzzy Set for Attribute Reduction From Decision Tables

Attribute reduction from decision tables is one of the crucial topics in data mining. This problem belongs to NP-hard and many approximation algorithms based on the filter or the filter-wrapper approaches have been designed to find the reducts. Intuitionistic fuzzy set (IFS) has been regarded as the effective tool to deal with such the problem by adding two degrees, namely the membership and non-membership for each data element. The separation of attributes in the view of two counterparts as in the IFS set would increase the quality of classification and reduce the reducts. From this motivation, this paper proposes a new filter-wrapper algorithm based on the IFS for attribute reduction from decision tables. The contributions include a new instituitionistics fuzzy distance between partitions accompanied with theoretical analysis. The filter-wrapper algorithm is designed based on that distance with the new stopping condition based on the concept of delta-equality. Experiments are conducted on the benchmark UCI machine learning repository datasets.

Optimization of the C4.5 Algorithm by Using a Genetic Algorithm for the Diagnosis of Life Expectancy for Hepatitis Patients

As technology develops rapidly, the amount of data generated experiencing rapid development, including medical data. Data can help diagnose the life expectancy of people with the disease such as hepatitis using data mining methods in the medical field. In this research, technique data mining uses a classification technique with the C4.5 algorithm and the UCI Machine Learning Repository dataset. This dataset has 19 attributes, 1 class, and 155 samples. C4.5 algorithm is optimized using the Genetic Algorithm feature selection process. This study compares the accuracy of the C4.5 algorithm before and after optimization using a Genetic Algorithm. C4.5 algorithm produces the highest accuracy of 96.23%. Meanwhile, the C4.5 algorithm, after being optimized using Genetic Algorithm, has the highest accuracy of 98.11%. The number of features selected is 15 features. Application of Genetic Algorithms in C4.5 algorithm is proven to improve the accuracy in diagnosing life expectancy of people with hepatitis as much as 1.88%.

Generalized intuitionistic fuzzy c-means clustering algorithm using an adaptive intuitionistic fuzzification technique

In real-world scenario, mostly, the datasets are either imprecise or uncertain in their original form. Due to this reason, the clustering of such datasets is unsatisfactory and we often get compromised results. The information present in the dataset can be made precise and useful with the popular generalization of fuzzy sets known as Atanassov intuitionistic fuzzy sets (AIFSs). Therefore, the AIFS-based c-means clustering algorithm becomes a convenient way for clustering uncertain and vague datasets. In the paper, we propose an intuitionistic fuzzy-based algorithm, namely Generalized Intuitionistic Fuzzy c-Means (G-IFCM) clustering algorithm which uses an adaptive AIFS Euclidean distance measure in its criterion function to cluster the dataset under intuitionistic fuzzy environment. The proposed intuitionistic fuzzification method incorporates a technique to transform the dataset into AIFS and maintains its original structure that tends to change during any fuzzification process. Further, simulation experiments are also conducted on few UCI machine learning repository datasets using G-IFCM and its performance is compared with some known clustering algorithms. The efficacy of the algorithm is tested with some popular benchmark indexes that check the cluster validity and clustering performance of G-IFCM. Finally, the computation suggests efficient performance of G-IFCM where the best choice of fuzzy factor, m probably lies in the interval [1, 2] that indicates reduction in the expense of computation.

A novel approach to build accurate and diverse decision tree forest.

Decision tree is one of the best expressive classifiers in data mining. A decision tree is popular due to its simplicity and straightforward visualization capability for all types of datasets. Decision tree forest is an ensemble of decision trees. The prediction accuracy of the decision tree forest is more than a decision tree algorithm. Constant efforts are going on to create accurate and diverse trees in the decision tree forest. In this paper, we propose Tangent Weighted Decision Tree Forest (TWDForest), which is more accurate and diverse than random forest. The strength of this technique is that it uses a more accurate and uniform tangent weighting function to create a weighted decision tree forest. It also improves performance by taking opinions from previous trees to best fit the successor tree and avoids the toggling of the root node. Due to this novel approach, the decision trees from the forest are more accurate and diverse as compared to other decision forest algorithms. Experiments of this novel method are performed on 15 well known, publicly available UCI machine learning repository datasets of various sizes. The results of the TWDForest method demonstrate that the entire forest and decision trees produced in TWDForest have high prediction accuracy of 1–7% more than existing methods. TWDForest also creates more diverse trees than other forest algorithms.

A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network

Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from this problem. Many Machine Learning (ML) approaches are not sufficient to forecast the disease caused by the virus. Therefore, there is a need for one system that predicts disease efficiently. The Deep Learning approach predicts the disease caused by the blocked heart. This paper proposes a Convolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on a comparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and the proposed prediction model of CNN. The UCI machine learning repository dataset for experimentation and Cardiovascular Disease (CVD) predictions with 94% accuracy.

ODRA: an outlier detection algorithm based on relevant attribute analysis method

Advances in data acquisition have generated an enormous amount of data that captures business, commercial, technological and scientific information. However, some occurrences are rare or unusual, irrespective of a large amount of data available. These rare occurrences in data mining are usually referred to as outliers or anomalies. All these rare occurrences are infrequent. Sometimes it varies from 0.01% to 10% depending on the type of application. In recent years, outlier detection has become important in many applications and has attracted considerable attention among the increasing number of data mining techniques. Focusing on this has resulted in several outlier detection algorithms, mostly based on distance or density. However, each method has its inherent weaknesses. Methods based on distance have problems with local density, and methods based on density have problems with low-density patterns. In this paper, we present a new outlier detection algorithm based on the relevant attribute analysis (ODRA) for local outlier detection in a high-dimensional dataset. There are two phases of the proposed algorithm. During the preliminary stage, we present a data reduction method that reduces the data set by pruning irrelevant attributes and data points. In the second phase, we propose an outlier detection method based on k-NN kernel density estimation. The experimental results on 15 UCI machine learning repository datasets show the supremacy and effectiveness of our proposed approach over state-of-the-art outlier detection methods.

Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.

ANALISIS PERFORMA METODE K-NEAREST NEIGHBOR UNTUK IDENTIFIKASI JENIS KACA

Nowadays, the industry makes various types of goods that have glass-based materials, float car window panes, non-float building windows, lamps, jars, and tableware. These glasses have the same production material, the difference between one and the other is the composition of the production material. K-Nearest Neighbor (KNN) algorithm which is one of the classification methods in data mining and also a supervised learning algorithm in machine learning is a method for classifying objects based on learning data that is the closest distance to the object.. This study discusses the performance measurement (accuracy, precision, recall and f-measure) of the KNN method with a variety of values on 1000 glass type production data objects obtained from the central UCI Machine Learning Repository dataset. The conclusion of this research is the results of the value of K = 3 to K = 9, the best performance values obtained at K = 3, where the level of accuracy reaches 64%, 63% precision, 71% recall, and F-Measure of 67%.

A new clustering method based on the bio‐inspired cuttlefish optimization algorithm

AbstractMost of the well‐known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio‐inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K‐means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA‐Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.

On the Enhancement of Classification Algorithms Using Biased Samples

Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed.