Data Mining - Concepts and Techniques.

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 997073, 980488 and 972683% respectively A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 938537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones

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Prediction of heart disease and classifiers’ sensitivity analysis

Correlation based feature selection with clustering for high dimensional data

Big data analytics gain significant interest over the traditional data-processing methodologies that engage in extracting the hidden patterns and correlations from the massive data, termed as big data. With the aim of relieving the computational complexity the clustering method plays a significant role. With the knowledge of the clustering algorithms, the big data arriving from the distributed sources is processed using the MapReduce framework (MRF). The MRF possesses two functions, namely, map function and reduce function, such that the map function is based on the proposed Fractional Sparse Fuzzy C-Means (FrSparse FCM) algorithm and reduce function is based on particle swarm optimisation-based whale optimisation algorithm (P-Whale). Initially, the optimal centroids are computed using the proposed algorithm in the mapper phase that is optimally tuned in the reducer phase, and it is clear that the proposed FrSparse FCM-based MRF ensures the parallel processing of the big data. Experimentation is performed using the Skin data set and the localisation data set taken from the UCI machine learning repository, and the analysis is progressed using the metrics, such as accuracy and DB Index. The analysis proves that the proposed method acquired a maximum accuracy of 90.6012% and a minimum DB Index of 5.33.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0899

MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm

The ultimate aim of the proposed method is to establish a model for classification of medical data In this paper we present ANN for Diagnosis of Heart disease ANN works like the neural arrangement of brain The mind studies from its past experiences It can solve the problems that are not computable or solvable by current computing systems Feature selection is most important process for selecting a relevant attribute among the huge data set This reduced set of attributes is then passed to artificial neural network ANN uses back propagation algorithm for better prediction of result, also the ANN is based on the knowledge this advantage can be taken for more accurate result ANN provides better accuracy The result given by ANN is highly accurate, so it may be charity in the therapeutic area for likelihood of diseases like swine flu and heart disease

An Artificial Neural Network Based Pattern Classification Algorithm for Diagnosis of Heart Disease

Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.

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Efficient Feature Subset Selection Algorithm for High Dimensional Data

Classification of health data for perfect opinion is a on the rise field of relevance and investigate in records removal. Medical data classification is an important data mining problem being discussed about for a decade that has attracted several researchers around the world. The sorting techniques provide invaluable information to pathologist for diagnosis and treatment of diseases. By detecting and counting blood cell within the blood smear using sorting techniques, it is quite possible to detect so many diseases. The aspiration of projecting data mining in scientific drug is to take models that can use patient exact information sequence, then system will be predict the conclusion of interest and to thereby support scientific administrative. The various data mining algorithms like Classification, Clustering, Regression techniques, soft computing and many more are used in health data sorting. According paper, we have literature survey some of the methods correlated to health data sorting. The chief heart of survey is to scrutinize DMT like ANN, decision tree, support vector machine, nearest neighbor, Bayesian algorithm required for health statistics removal above all to determine nearby regular diseases such as swine flu, heart disease, lung tumor, dengue, breast growth tumor and so on.

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Sudarson Jena

Papers

Correlation based feature selection with clustering for high dimensional data

MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm

An Artificial Neural Network Based Pattern Classification Algorithm for Diagnosis of Heart Disease

Efficient Feature Subset Selection Algorithm for High Dimensional Data

Medical data classification using different optimization techniques: a survey