Data Mining Practical Machine Learning Tools and Techniques

The presence of missing values in real-world data is not only a prevalent problem but also an inevitable one. Therefore, missing values should be handled carefully before the mining or learning process. This paper proposes a novel technique to impute missing data. It employs a new version of Fuzzy c-Means clustering algorithm which benefits from advantages of Grey Relational Grade over Minkowski-like similarity measures. To impute a missing value more accurately, it also performs a local mutual information based feature selection in each cluster to select only highly relevant features. Briefly, missing values are imputed in the following steps. First, the algorithm finds the importance of each missing attribute. Next, input instances are separated into several fuzzy clusters. Then, the algorithm selects clusters which satisfy a minimum condition. After that, it chooses highly dependent features of instances within each cluster using a mutual information based feature selection approach. When the features are selected, regression models will be applied to the selected features of the selected clusters to provide estimations for a missing value. Finally, the missing value is imputed through a weighted average of estimated values obtained from the previous step. Three well-known evaluation criteria and the accuracy of classification task are used to assess the performance of the proposed method. The experimental results for seven UCI data sets with different missing ratios and strategies indicate that the proposed algorithm outperforms five other imputation methods in general.

Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model

A comprehensive guide to music therapy : Theory, clinical practice, research and training

A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S026988891800036X

Data preprocessing in predictive data mining

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique.

Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

In a ubiquitous environment, high-accuracy data analysis is essential because it affects real-world decision-making. However, in the real world, user-related data from information systems are often missing due to users’ concerns about privacy or lack of obligation to provide complete data. This data incompleteness can impair the accuracy of data analysis using classification algorithms, which can degrade the value of the data. Many studies have attempted to overcome these data incompleteness issues and to improve the quality of data analysis using classification algorithms. The performance of classification algorithms may be affected by the characteristics and patterns of the missing data, such as the ratio of missing data to complete data. We perform a concrete causal analysis of differences in performance of classification algorithms based on various factors. The characteristics of missing values, datasets, and imputation methods are examined. We also propose imputation and classification algorithms appropriate to different datasets and circumstances.

/pdf/missing-values-and-optimal-selection-of-an-imputation-method-2rcj2r2h7e.pdf

Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

Selection of the optimal combination of imputation method and classifier is very costly.A novel method of automatic, adaptive selection of the optimal combination, AMCI, is proposed.Successfully demonstrate the superiority of the proposed method with multiple data sets.The results also suggest that AMCI is scalable: good for bid data analytics and IoT applications. Classifiers and imputation methods have played crucial parts in the field of big data analytics. Especially, when using data sets characterized by horizontal scattering, vertical scattering, level of spread, compound metric, imbalance ratio and missing ratio, how to combine those classifiers and imputation methods will lead to significantly different performance. Therefore, it is essential that the characteristics of data sets must be identified in advance to facilitate selection of the optimal combination of imputation methods and classifiers. However, this is a very costly process. The purpose of this paper is to propose a novel method of automatic, adaptive selection of the optimal combination of classifier and imputation method on the basis of features of a given data set. The proposed method turned out to successfully demonstrate the superiority in performance evaluations with multiple data sets. The decision makers in big data analytics could greatly benefit from the proposed method when it comes to dealing with data set in which the distribution of missing data varies in real time.

Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

Healthcare and wellness industries have become more promising as the interests on healthy living increase. Not only the medical care oriented services for the patients done by medical centers but also the psychological and emotional healthgiving services for the people who are normal have been being stressed. The psychological and emotional healthgiving services should be executed in an agile and timely manner to maximize its effects. This paper aims to propose an emotion healing service spaces which are able to provide the normal people with psychological care services. To achieve the goals, we invented the tripot approach : the ubiquitous computing technology for context-aware and intelligent estimation of psychological index, LED technology to implement emotional atmosphere and wellness healthcare technology. The proposed architecture has been implemented in an actual site.

Converging Ubiquitous Computing and LED Technologies for Wellness Emotional Space Service Providing Health Therapies

Recently, as the development of Ubiquitous Smart Space (USS) such as U-City is proliferating, preparing a development methodology dedicated to USS characteristics has become one of the crucial issues to increase the productivity of the USS projects. However, since the legacy development methodologies such as traditional UML do not consider the critical components of ubiquitous computing space such as context-awareness, service oriented architecture and multi-agent communication, they are not optimized for USS analysis and design. Hence, the purpose of this paper is to propose a USS-sophisticated development methodology. To do so, a hybrid approach, BPR-USS for space reengineering and UML-USS by amending UML with corresponding toolkit based on StarUML has been implemented. To show the feasibility of the approach described in this paper, an illustrative example with an actual USS development project for a multiplex space in Seoul is shown.

https://www.koreascience.kr:443/article/JAKO200706414116730.pdf

Jaemun Sim

Papers

Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

Converging Ubiquitous Computing and LED Technologies for Wellness Emotional Space Service Providing Health Therapies

Space Reengineering and Amended UML Approach to Requirement Analysis for Ubiquitous Smart Space Development

Discussion on Achieving Resistance Spot Welding Knowledge Completion