UCI Machine Learning Repository Datasets Research Articles

Time series classification using local distance-based features in multi-modal fusion networks

We propose the use of a novel feature, called local distance features, for time series classification. The local distance features are extracted using Dynamic Time Warping (DTW) and classified using Convolutional Neural Networks (CNN). DTW is classically as a robust distance measure for distance-based time series recognition methods. However, by using DTW strictly as a global distance measure, information about the matching is discarded. We show that this information can further be used as supplementary input information in temporal CNNs. This is done by using both the raw data and the features extracted from DTW in multi-modal fusion CNNs. Furthermore, we explore the effects of different prototype selection methods, prototype numbers, and data fusion schemes induce on the accuracy. We perform experiments on a wide range of time series datasets including three Unipen handwriting datasets, four UCI Machine Learning Repository datasets, and 85 UCR Time Series Classification Archive datasets.

An intelligent method for iris recognition using supervised machine learning techniques

In the new millennium, with chaotic situation that exist in the world, people are threaten with multifarious terrorist attacks. There have been several intelligent ways in order to recognize and diminish these assaults wisely. Biometric traits have proven to be one of the useful ways for tackling these problems. Among all these traits, the iris recognition systems are appropriate tools for the human identification not only the iris pattern is well-known features, but also it has numerous features such as compactness representation, uniqueness texture, and stability. In spite of the fact that there have been published many approaches in these areas, there are still abundant problems in these approaches like time consuming, and computational complexity. In order to solve these obstacles, we propose the extravagant iris recognition methods that are based on combination of two dimensional Gabor kernel (2-DGK), step filtering (SF) and polynomial filtering (PF) for feature extraction and hybrid radial basis function neural network (RBFNN) with genetic algorithm (GA) for matching task. To assess the performance of the proposed method, we use two benchmarks in our algorithm and implemented it on CASIA-Iris V3, UBIRIS. V1 and UCI machine learning repository datasets. The experimental results of the proposed method reveal that the method is efficient in the iris recognition.

An improved runner-root algorithm for solving feature selection problems based on rough sets and neighborhood rough sets

Solving the feature selection problem is considered an important issue when addressing data from real applications that contain a large number of features. However, not all of these features are important; therefore, the redundant features must be removed because they affect the accuracy of the data representation and introduce time complexity into the analysis of these data. For these reasons, the feature selection problem is considered an NP-complete nonlinearly constrained optimization problem. The rough set (RS) and neighborhood rough set (NRS) are the most powerful methods used to solve the feature selection problem; however, both approaches suffer from high time complexity. To avoid these limitations, we combined the RS and NRS with a new metaheuristic algorithm called the runner-root algorithm (RRA). The spirit of the RRA originated from real-life plants called running plants, which have roots and runners that spread the plants in search of minerals and water resources through their root and runner development. To validate the proposed algorithm, several UCI Machine Learning Repository datasets are used to compute the performance of our algorithm employing two effective classifiers, the random forest and the K-nearest neighbor, in addition to some other measures for the performance evaluation. The experimental results illustrate that the proposed algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures. Additionally, the NRS increases the performance of the proposed method more than the RS as an objective function.

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved.

Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

The use of iris tissue for identification is an accurate and reliable system for identifying people. This method consists of four main processing stages, namely segmentation, normalization, feature extraction, and matching. In this study, a new method of feature extraction and classification based on gray-level difference method and hybrid MLPNN-ICA classifier is proposed. For experimental results, our study is implemented on CASIA-Iris V3 dataset and UCI machine learning repository datasets.

Hypergraph Learning With Cost Interval Optimization

In many classification tasks, the misclassification costs of different categories usually vary significantly. Under such circumstances, it is essential to identify the importance of different categories and thus assign different misclassification losses in many applications, such as medical diagnosis, saliency detection and software defect prediction. However, we note that it is infeasible to determine the accurate cost value without great domain knowledge. In most common cases, we may just have the information that which category is more important than the other categories, i.e., the identification of defect-prone softwares is more important than that of defect-free. To tackle these issues, in this paper, we propose a hypergraph learning method with cost interval optimization, which is able to handle cost interval when data is formulated using the high-order relationships. In this way, data correlations are modeled by a hypergraph structure, which has the merit to exploit the underlying relationships behind the data. With a cost-sensitive hypergraph structure, in order to improve the performance of the classifier without precise cost value, we further introduce cost interval optimization to hypergraph learning. In this process, the optimization on cost interval achieves better performance instead of choosing uncertain fixed cost in the learning process. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.

Incremental fuzzy cluster ensemble learning based on rough set theory

To deal with the uncertainty, vagueness and overlapping distribution within the data sets, a novel incremental fuzzy cluster ensemble method based on rough set theory (IFCERS) is proposed by the idea of combining clustering analysis task with classification techniques. Firstly, on the basis of soft clustering results, the positive region, boundary region and negative region of clustering ensemble are obtained by applying the construction of rough approximation in rough set theory, and then a group structure within data points of positive region is obtained by adopting a fuzzy cluster ensemble method. Secondly, by combining with the supervised ensemble learning method, e.g., random forests, the obtained group structure is used to construct the random forests classifier to classify the data points in boundary region. Finally, all the acquired group structure is used to train the random forests classifier to classify the data points of negative region. Experimental evaluations on UCI machine learning repository datasets verify the effectiveness of the proposed method. It is also shown that the quality of the final solution has a weak correlation with the ensemble size, the parameter setting on the rough approximations construction is appropriate, and the proposed method is robust towards the diversity from hard clustering members.

Ensemble feature selection using bi-objective genetic algorithm

Feature selection problem in data mining is addressed here by proposing a bi-objective genetic algorithm based feature selection method. Boundary region analysis of rough set theory and multivariate mutual information of information theory are used as two objective functions in the proposed work, to select only precise and informative data from the data set. Data set is sampled with replacement strategy and the method is applied to determine non-dominated feature subsets from each sampled data set. Finally, ensemble of such bi-objective genetic algorithm based feature selectors is developed with the help of parallel implementations to produce much generalized feature subset. In fact, individual feature selector outputs are aggregated using a novel dominance based principle to produce final feature subset. Proposed work is validated using repository especially for feature selection datasets as well as on UCI machine learning repository datasets and the experimental results are compared with related state of art feature selection methods to show effectiveness of the proposed ensemble feature selection method.

Ensemble Method of Effective AdaBoost Algorithm for Decision Tree Classifiers

This article introduces a novel ensemble method named eAdaBoost (Effective Adaptive Boosting) is a meta classifier which is developed by enhancing the existing AdaBoost algorithm and to handle the time complexity and also to produce the best classification accuracy. The eAdaBoost reduces the error rate when compared with the existing methods and generates the best accuracy by reweighing each feature for further process. The comparison results of an extensive experimental evaluation of the proposed method are explained using the UCI machine learning repository datasets. The accuracy of the classifiers and statistical test comparisons are made with various boosting algorithms. The proposed eAdaBoost has been also implemented with different decision tree classifiers like C4.5, Decision Stump, NB Tree and Random Forest. The algorithm has been computed with various dataset, with different weight thresholds and the performance is analyzed. The proposed method produces better results using random forest and NB tree as base classifier than the decision stump and C4.5 classifiers for few datasets. The eAdaBoost gives better classification accuracy, and prediction accuracy, and execution time is also less when compared with other classifiers.

Random bits regression: a strong general predictor for big data

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regression on those intermediate/derived features to predict the outcome. Benchmark analyses on a simulated dataset, UCI machine learning repository datasets and a GWAS dataset showed that RBR outperforms other popular methods in accuracy and robustness. RBR (available on https://sourceforge.net/projects/rbr/) is very fast and requires reasonable memories, therefore, provides a strong, robust and fast predictor in the big data era.

Picture inference system: a new fuzzy inference system on picture fuzzy set

In this paper, we propose a novel fuzzy inference system on picture fuzzy set called picture inference system (PIS) to enhance inference performance of the traditional fuzzy inference system. In PIS, the positive, neutral and negative degrees of the picture fuzzy set are computed using the membership graph that is the combination of three Gaussian functions with a common center and different widths expressing a visual view of degrees. Then, the positive and negative defuzzification values, synthesized from three degrees of the picture fuzzy set, are used to generate crisp outputs. Learning in PIS including training centers, widths, scales and defuzzification parameters is also discussed. The system is adapted for all architectures such as the Mamdani, the Sugeno and the Tsukamoto fuzzy inferences. Experimental results on benchmark UCI Machine Learning Repository datasets and an example in control theory - the Lorenz system are examined to verify the advantages of PIS.

Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection

this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes. KeywordsMining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.

A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis

In this paper, a new similarity measure and a weighted similarity measure on intuitionistic fuzzy soft sets (IFSSs) are proposed and some of their basic properties are discussed. Using the proposed similarity measure, a relation (≈α) between two IFSSs are defined and it is found that the defined relation is not an equivalence relation. Further, the effectiveness of the proposed similarity measure is demonstrated in a numerical example with the help of measure of performance and measure of error. Moreover, medical diagnosis problems have been exhibited through a hypothetical case study by using this proposed similarity measure. Finally, the proposed method is applied to 10 different medical data sets from UCI Machine Learning Repository datasets and its similarity measures are calculated. The corresponding performance measures, like, accuracy, sensitivity, specificity, ROC curves, AUC values, and F-measures are obtained and it is compared with the existing methods. This shows that the proposed method exhibits more accuracy, sensitivity and enhanced F-measures than the existing methods.

A Novel Multiple Fuzzy Clustering Method Based on Internal Clustering Validation Measures with Gradient Descent

In this paper, we propose a novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. Firstly, some single fuzzy clustering algorithms such as Fuzzy C-Means, Kernel Fuzzy C-Means and Gustafson–Kessel are used to construct similarity matrixes for each partition. Secondly, those similarity matrixes are aggregated into a final one by means of the direct sum of weighted vectors where the values of weights are determined by internal clustering validation measures. Finally, final membership matrix is calculated by minimizing the sum of square errors through the gradient descent method. The proposed approach has been validated in terms of clustering quality on UCI Machine Learning Repository datasets. Experimental results show that the proposed approach’s performance is better than those of other ensemble and standalone methods.

Discriminant error correcting output codes based on spectral clustering

Error correcting output codes (ECOCs) is a powerful framework to solve the multi-class problems. Finding the optimal partitions with maximum class discrimination efficiently is a key point to improve its performance. In this paper, we propose an alternative and efficient approach to obtain the partitions which are discriminative in the class space. The main idea of the proposed method is to transform the partition in the class space into the cut for an undirected graph using spectral clustering. In addition to measuring the class similarity, the confusion matrix with a pre-classifier is used. Our method is compared with the classical ECOC and DECOC over a synthetic dataset, a set of UCI machine learning repository datasets and one face recognition application. The results show that our proposal is able to obtain comparable or even better classification accuracy while reducing the computational complexity in comparison with the state-of-the-art coding methods.

A novel combining classifier method based on Variational Inference

In this paper, we propose a combining classifier method based on the Bayesian inference framework. Specifically, the outputs of base classifiers (called Level1 data or meta-data) are utilized in a combiner to produce the final classification. In our ensemble system, each class in the training set induces a distribution on the Level1 data, which is modeled by a multivariate Gaussian distribution. Traditionally, the parameters of the Gaussian are estimated using a maximum likelihood approach. However, maximum likelihood estimation cannot be applied since the covariance matrix of Level1 data of each class is not full rank. Instead, we propose to estimate the multivariate Gaussian distribution of Level1 data of each class by using the Variational Inference method. Experiments conducted on eighteen UCI Machine Learning Repository datasets and a selected 10-class CLEF2009 medical imaging database demonstrated the advantage of our method compared with several well-known ensemble methods.

Outlier detection using neighborhood rank difference

Presence of outliers critically affects many pattern classification tasks. In this paper, we propose a novel dynamic outlier detection method based on neighborhood rank difference. In particular, reverse and the forward nearest neighbor rank difference is employed to capture the variations in densities of a test point with respect to various training points. In the first step of our method, we determine the influence space for a given dataset. A score for outlierness is proposed in the second step using the rank difference as well as the absolute density within this influence space. Experiments on synthetic and some UCI machine learning repository datasets clearly indicate the supremacy of our method over some recently published approaches.

A subspace approach to error correcting output codes

Among the proposed methods to deal with multi-class classification problems, the error-correcting output codes (ECOCs) represents a powerful framework. A key factor in designing any ECOC matrix is the independency of the binary classifiers, without which the ECOC method would be ineffective. This paper proposes an efficient new approach to the classical ECOC design in order to improve independency among classifiers. The main idea of the proposed method is based on using different feature subsets for each binary classifier, named subspace ECOC. In addition to creating more independent classifiers in the proposed technique, ECOC matrices with longer codes can be built. The numerical experiments in this study compare the classification accuracy of subspace ECOC, classical ECOC, one-versus-one, and one-versus-all methods over a set of UCI machine learning repository datasets and two image vision applications. The results show that the proposed technique increases the classification accuracy in comparison with the state of the art coding methods.

Classification Performance of Bagging and Boosting Type Ensemble Methods with Small Training Sets

Classification performance of an ensemble method can be deciphered by studying the bias and variance contribution to its classification error. Statistically, the bias and variance of a single classifier is controlled by the size of the training set and the complexity of the classifier. It has been both theoretically and empirically established that the classification performance (hence bias and variance) of a single classifier can be improved partially by using a suitable ensemble method of the classifier and resampling the original training set. In this paper, we have empirically examined the bias-variance decomposition of three different types of ensemble methods with different training sample sizes consisting of 10% to maximum 63% of the observations from the original training sample. First ensemble is bagging, second one is a boosting type ensemble named adaboost and the last one is a bagging type hybrid ensemble method, called bundling. All the ensembles are trained on training samples constructed with small subsampling ratios (SSR) 0.10, 0.20, 0.30, 0.40, 0.50 and bootstrapping. The experiments are all done on 20 UCI Machine Learning repository datasets and designed to find out the optimal training sample size (smaller than the original training sample) for each ensemble and then find out the optimal ensemble with smaller trianing sets with respect to the bias-variance performance. The bias-variance decomposition of bundling shows that this ensemble method with small subsamples has significantly lower bias and variance than subsampled and bootstrapped version of bagging and adaboost.

Feature selection based on linear discriminant analysis

The paper proposed a new approach of feature selection based on Constrained Linear Discriminant Analysis(CLDA),which modeled feature selection as a search problem in subspace and made optimal solution subject to some restrictions.Furthermore,CLDA optimization problem was transformed into a process of scoring and sorting features.Experiments on UCI machine learning repository and Reuters-21578 dataset show that the proposed approach can consistently obtain better results with fewer features than that with all features.