Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Pattern Identification on various domains have become one of the most researched fields. Accuracy of all traditional and standard classifiers is highly proportional to the completeness or quality of the training data. Completeness is bound by various parameters such as noise, highly representative samples of the real world population, availability of training data, dimensionality etc. Another very pressing and domineering issue identified in real world data sets is that the data is well-dominated by typical occurring examples but with only a few rare or unusual occurrences. This distribution among classes make the real world data inherently imbalanced in many domains like medicine, finance, marketing, web, fault detection, anomaly detection etc. This chapter aims to highlight the existence of imbalance in all real world data and the need to focus on the inherent characteristics present in imbalanced data that can degrade the performance of classifiers. It provides an overview of the existing effective methods and solutions implemented towards the significant problems of imbalanced data for improvement in the performance of standard classifiers. Efficient metrics for evaluating the performance of imbalanced learning models followed by future directions for research is been highlighted.

Learning From Imbalanced Data

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

The structure

A basic model of the transport phenomena occurring during solidification of multicomponent mixtures is presented. The model is based on a two-phase approach, in which each phase is treated separately and interactions between the phases are considered explicitly. The macroscopic transport equations for each phase are derived using the technique of volumetric averaging. The basic forms of the constitutive relations are developed. These relations link the macroscopic transport phenomena to microscopic processes such as microstructure development, interfacial stresses, and interfacial heat and mass transfer. Thermodynamic relations are presented, and it is shown that nonequilibrium effects can be addressed within the framework of the present model. Various simplifications of the model are examined, and future modeling needs are discussed.

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A volume-averaged two-phase model for transport phenomena during solidification

ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data

Extension of the continuum model for transport phenomena occurring during metal alloy solidification—I. The conservation equations

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (as Bagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in as Bagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Jun Ni

Papers

A volume-averaged two-phase model for transport phenomena during solidification

ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data

Extension of the continuum model for transport phenomena occurring during metal alloy solidification—I. The conservation equations

An improved ensemble learning method for classifying high-dimensional and imbalanced biomedicine data

Extension of the continuum model for transport phenomena occurring during metal alloy solidification-II. Microscopic considerations