Approach For Classification Of Data Research Articles

A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING

During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.

Multiple instance learning for sequence data with across bag dependencies

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory.

Psychiatry on Heart Issue and Different Data Classification Prediction with Scheduled Cause of Diabetes

Despite the fact existence of an express heartrending world, living prototype and culture of Human applied with various states of affairs, the traditional view of accepted wisdom with a different ideology based on their own procession of thoughts to survive their life with high mental stress. Data Classification approaches and methodologies are studied over them and planned to apply new methodologies in the future. Nowadays, Diabetes plays a very important role, which affects based on all age categories of people easily. Once debate is identified, the whole routine culture gets distressed. Infections towards some organs unvoiced in the human body, some evidence is required to apply and classify the system easily and safeguard human life as much as safe, to avoid heart-related issues.

Time Conserving Multi-Label Classification System by Incorporating Pyramid Data Structure

Data classification is one of the evergreen research areas of data analysis. Numerous data classification approaches exist in the literature and most of the classification systems are based on binary and multi-class classification. Multi-label classification system attempts to suggest multiple labels for a single entity. However, it is complex to attain a better multi-label classification system. Taking this as a challenge, this work proposes a multi-label classification system, which extracts the features of both entities and labels. The relationship between them are organised in the pyramid data structure. As the features are organized effectively, the interrelated labels are present in the same tier. This feature makes it simple for suggesting multiple labels for a single entity. The performance of this work is analysed over three different datasets and compared against existing approaches in terms of precision, recall, accuracy and time consumption.

An efficient hybrid deep learning approach for internet security

Nowadays, internet is mostly used communication tool worldwide. However, the major problem of the internet is to provide security. To provide internet security, many researches and papers have been suggested about information and network security. The commonly used system against network attacks is firewalls.In this study, a novel firewall data classification approach is presented. This approach uses 10 cases to obtain numerical results. The proposed approach consists of data acquisition from Firewall, feature selection and classification steps. Firstly, the Firewall data were gathered from a Firewall. Then, the redundant features are eliminated and these features are normalized using min–max normalization. The obtained final feature sets are forwarded to classifiers. In the cases defined, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Support Vector Machine (SVM) are utilized as classifiers. It was seen from the results, the deep learning approach are more successful than SVM classifier and the highest classification accuracy was calculated as 97.38% by using Bi-LSTM-LSTM hybrid network. The proposed method has several advantages and these are (1) the proposed method achieved high success rates using hybrid deep learning approaches (2) the training time of the proposed method is short (3) an intelligent network security monitoring method is presented using basic methods and deep learning. In addition, a useful approach has been presented to achieve high success rate at the end of the faster training process than traditional machine learning methods. Briefly, an intelligent monitoring system is proposed for network security.

A Novel Approach for Big Data Classification and Transportation in Rail Networks

This paper introduces a new framework into future data-driven railway condition monitoring systems (RCM). For this purpose, we have proposed an edge processing unit that includes two main parts: a data classification model that classifies Internet of Things (IoT) data into maintenance-critical data (MCD) and maintenance-non-critical data (MNCD) and a data transmission unit that, based on the class of data, employs appropriate communication methods to transmit data to railway control centers. For the transmission of MNCD, we propose a travel pattern method that employs train stations as points of data offloading so that trains can deliver data as well as passengers at stations. The performance of our proposed solution is successfully validated via three various data sets under different operating conditions.

A new big data approach for topic classification and sentiment analysis of Twitter data

Twitter is a major micro-blogging service, with millions of active users. These users use Twitter to post status messages called tweets and share their opinions using hash tags on various events. Hence, Twitter is considered a major real time streaming source and one of an effective and accurate indicator of opinions. The amount of data generated by Twitter is huge and it is difficult to scan entire data manually. This paper proposes a Hybrid Lexicon-Naive Bayesian Classifier (HL-NBC) method for sentimental analysis. In addition to that, Sentiment analysis engine is preceded by topic classification, which classifies tweets into different categories and filters irrelevant tweets. The proposed method is compared with Lexicon, Naive Bayesian classifier for uni-gram and bi-gram features. Out of the different approaches, the proposed HL-NBC method does sentiment classification in an improved way and gives accuracy of 82%, which is comparatively better than other methods. Also, the sentiment analysis is performed in a shorter time compared to traditional methods and achieves 93% improvement in processing time for larger datasets.

Document Classification Using Artificial Neural Network

The Document classification system is the field of data mining in which the format of data is based on bag of words (BoW) or document vector model and the task is to build a machine which after successfully learn the characteristic of given data set, predicts the category of the document to which the word vector belongs. In this approach document is represented by BoW where every single word is used as feature which occurs in a document. The proposed article presents artificial neural network approach which is hybrid of n-fold cross validation and training-validation-test approach for classification of data.

Ensemble RBM-based classifier using fuzzy integral for big data classification

The restricted Boltzmann machine (RBM) is a primary building block of deep learning models. As an efficient representation learning approach, deep RBM can effectively extract sophisticated and informative features from raw data. Little research has been undertaken on using deep RBM to extract features from big data however. In this paper, we investigate this problem, and an ensemble approach for big data classification based on Hadoop MapReduce and fuzzy integral is proposed. The proposed method consists of two stages, map and reduce. In the map stage, multiple RBM-based classifiers used for ensemble are trained in parallel. In the reduce stage, the trained multiple RBM-based classifiers are integrated by fuzzy integral. Experiments on five big data sets show that the proposed approach can outperform other baseline methods to achieve state-of-the-art performance.

Novel machine learning approach for classification of high-dimensional microarray data

Independent component analysis (ICA) is a powerful concept for reducing the dimension of big data in many applications. It has been used for the feature extraction of microarray gene expression data in numerous works. One of the merits of ICA is that a number of extracted features are always equal to the number of samples. When ICA is applied to microarray data, whenever, it faces the challenges of how to find the best subset of genes (features) from extracted features. To resolve this problem, in this paper, we propose a new (artificial bee colony) ABC-based feature selection approach for microarray data. Our approach is based on two stages: ICA-based extraction approach to reduce the size of data and ABC-based wrapper approach to optimize the reduced feature vectors. To validate our proposed approach, extensive experiments were conducted to compare the performance of ICA + ABC with the results obtained from recently published and other previously suggested methods of gene selection for Naive Bayes (NB) classifier. To compare the performance of the proposed approach with other algorithms, a statistical hypothesis test was employed with six benchmark cancer classification datasets of the microarray. The experimental result shows that the proposed approach demonstrates an improvement over all the algorithms for NB classifier with a certain level of significance.

Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

For the spatial-spectral classification of hyperspectral images (HSIs), a deep learning framework is proposed in this study, which consists of convolutional neural networks (CNNs) and Markov random fields (MRFs). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilised as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic prior for regularising the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation. In comparison with several state-of-the-art approaches for data classification on three publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.

Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagno-sis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women’s Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.

Sequential minimal optimization classification approach for caesarian section classification dataset data set by applying various kernels

Sequential minimal optimization classification approach for caesarian section classification dataset data set by applying various kernels

A new complement naïve Bayesian approach for biomedical data classification

Biomedical data classification tasks are very challenging because data is usually large, noised and imbalanced. Particularly the noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. Accordingly, most existing learning algorithms have integrated various approaches to enhance their learning abilities from noisy environments, but the existence of noise can still introduce serious negative impacts. A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed. Therefore, we introduce a method called double learning to improve the classification performance of our model. As to the author’s knowledge, most of the previous works used the normal (noise free) instances for model construction (training) after the noise instances are isolated. This approach increases computational task on model construction for active learners and total computational time for passive learners. It also ignores minority data instance which leads to miss classification of instances from minority group as test cases. The main idea of this paper is to construct a model using noised instances. This approach minimizes the model construction time by reducing the number of instances and improves classification performance. Therefore, only the identified noised data are used for model construction instead of the normal (noise free) data. Since noised instances are used for model construction, the entire naive Bayesian working logic is reversed. This method is called complement naive Bayesian (CNB) which makes use of the idea of complement based learning to improve the accuracy performance. Finally, the performance of the proposed CNB is compared to naive Bayesian and some other classification algorithms with the single photon emission computed tomography, Indian liver patient dataset, Wilt and Tic-Tac-Toe endgame datasets. The experimental results demonstrated that the proposed approach has shown promising results in terms of computational time and accuracy performance on both balanced and imbalanced datasets used.

Clustering‐based approach for medical data classification

SummaryMedical data records are growing enormously in present days due to the growth of various medical technologies and population of the globe. It is a difficult task for a medical expert to process these data records to identify and provide treatment regarding the disease of patient. Hence, it is necessary to automate the processing of such medical data using machine learning‐based Medical Decision Support System. In this proposed work, an Effective Fuzzy Rule Classifier (EFRC)–based decision support system is used for analysis of UCI medical dataset for identification of disease of patients. Initially, the method determines the best centroid value for each of the attribute using training dataset. Furthermore, the centroid value is refined using test samples. The refined centroid values of all the attributes are used by the fuzzy classifier for medical data classification. Experimental results have proven that EFRC performs better classification than existing systems in terms of accuracy, sensitivity, and specificity.

Assessing Cropland Area in West Africa for Agricultural Yield Analysis

Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.

Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification

Classification of the high-dimensional data is challenging due to the curse of dimensionality, heavy computational burden and decreasing precision of algorithms. In order to mitigate these effects, feature selection approaches that can determine an efficient subset of features are utilized in the processing. However, most of these techniques attain just one subset of non-redundant features including the best ones. Alternatively, clustering approaches can be used to find the most informative clusters of features instead of generating just a single subset. So called, Hybrid Feature Clustering (HFC) method is capable of maximizing the classification accuracy while keeping the amount of redundant features in each cluster low. The patterns of each cluster are classified by a neural tree that employs Radial Basis Function (RBF) for the nodes. Within each neural tree, a hierarchical approach is proposed to transfer the knowledge of synaptic weights from a parent RBF node to each child. A gating network is applied on the forest of these neural trees in order to aggregate the results. By assessing the classification accuracy and the computational complexity on high-dimensional datasets it can be shown that the proposed solution has outperformed the state of the art classifiers. Furthermore, the computational complexity and the convergence of this method are theoretically proven and the robustness analysis under noisy conditions is conducted.

A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection

ABSTRACTLearning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class. Classifiers that proves efficient on standard data fail when the data is imbalanced as they are over trained by the majority class instances. Since class imbalance is a common characteristic of real-world data, the need for better classifiers becomes essential. This paper proposes a novel instance-based classification algorithm called Weighted Pattern Matching based Classification (PMC+) for classifying imbalanced data. PMC+ classifies unlabelled instances by computing the absolute difference between the feature values of the instances in the dataset and the unlabelled instance. PMC+ employs a simple classification procedure with weights and shows reasonably good performance. To improve the performance of PMC+, Fireworks based Feature and Weight Selection algorithm based on the idea of PMC+ has been proposed. PMC+ is evaluated on 44 binary imbalanced datasets and 15 multiclass imbalanced datasets. Although PMC+ does not employ a resampling or cost-sensitive method, experiments show that PMC+ is effective for classification of imbalanced data. The results of the experiments were validated using various non-parametric statistical tests.

A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data

Companies want to extract value from their relational databases. This is the aim of relational data mining. Propositionalization is one possible approach to relational data mining. Propositionalization adds new attributes, called features, to the main table, leading to an attribute-value representation, a single table, on which a propositional learner can be applied. However, current relational databases are large and composed of mixed, numerical and categorical, data. Moreover, the specificity of relational data is to involve one-to-many relationships. As an example of such data, consider customers purchasing products: each customer can purchase several products. Therefore, there is a need for techniques able to learn complex aggregates. Learning such features means to explore a combinatorial, possibly infinite, space and such an approach is prone to overfitting. We introduce a propositionalization approach dedicated to a robust Bayesian classifier. It efficiently samples a given number of features in the language bias, following a distribution over the complex aggregates. This distribution is also used to penalize complex aggregates in the regularization of the robust Bayesian classifier. Experiments show that it performs better than state-of-the-art methods on most investigated benchmarks and can deal with large datasets more easily. A new real, large, mixed relational dataset is introduced which confirms the ability of our approach to learn complex aggregates.

Using Persistent Homology as a New Approach for Super-Resolution Localization Microscopy Data Analysis and Classification of γH2AX Foci/Clusters.

DNA double strand breaks (DSB) are the most severe damages in chromatin induced by ionizing radiation. In response to such environmentally determined stress situations, cells have developed repair mechanisms. Although many investigations have contributed to a detailed understanding of repair processes, e.g., homologous recombination repair or non-homologous end-joining, the question is not sufficiently answered, how a cell decides to apply a certain repair process at a certain damage site, since all different repair pathways could simultaneously occur in the same cell nucleus. One of the first processes after DSB induction is phosphorylation of the histone variant H2AX to γH2AX in the given surroundings of the damaged locus. Since the spatial organization of chromatin is not random, it may be conclusive that the spatial organization of γH2AX foci is also not random, and rather, contributes to accessibility of special repair proteins to the damaged site, and thus, to the following repair pathway at this given site. The aim of this article is to demonstrate a new approach to analyze repair foci by their topology in order to obtain a cell independent method of categorization. During the last decade, novel super-resolution fluorescence light microscopic techniques have enabled new insights into genome structure and spatial organization on the nano-scale in the order of 10 nm. One of these techniques is single molecule localization microscopy (SMLM) with which the spatial coordinates of single fluorescence molecules can precisely be determined and density and distance distributions can be calculated. This method is an appropriate tool to quantify complex changes of chromatin and to describe repair foci on the single molecule level. Based on the pointillist information obtained by SMLM from specifically labeled heterochromatin and γH2AX foci reflecting the chromatin morphology and repair foci topology, we have developed a new analytical methodology of foci or foci cluster characterization, respectively, by means of persistence homology. This method allows, for the first time, a cell independent comparison of two point distributions (here the point distributions of two γH2AX clusters) with each other of a selected ensample and to give a mathematical measure of their similarity. In order to demonstrate the feasibility of this approach, cells were irradiated by low LET (linear energy transfer) radiation with different doses and the heterochromatin and γH2AX foci were fluorescently labeled by antibodies for SMLM. By means of our new analysis method, we were able to show that the topology of clusters of γH2AX foci can be categorized depending on the distance to heterochromatin. This method opens up new possibilities to categorize spatial organization of point patterns by parameterization of topological similarity.