A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions. First, we attempt to understand the nature of the class imbalance problem by establishing a relationship between concept complexity, size of the training set and class imbalance level. Second, we discuss several basic re-sampling or cost-modifying methods previously proposed to deal with the class imbalance problem and compare their effectiveness. The results obtained by such methods on artificial domains are linked to results in real-world domains. Finally, we investigate the assumption that the class imbalance problem does not only affect decision tree systems but also affects other classification systems such as Neural Networks and Support Vector Machines. <s> BIB001 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> We studied two prenatal data sets and two other medical data sets. Our objective was to increase sensitivity (accuracy of preterm birth) by changing the rule strength for the preterm birth class. Two criteria for choosing the optimal rule strength are discussed: the greatest difference between the true-positive and false-positive probabilities and the maximum profit. <s> BIB002 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing. <s> BIB003 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> There are several aspects that might influence the performance achieved by existing learning systems. It has been reported that one of these aspects is related to class imbalance in which examples in training data belonging to one class heavily outnumber the examples in the other class. In this situation, which is found in real world data describing an infrequent but important event, the learning system may have difficulties to learn the concept related to the minority class. In this work we perform a broad experimental evaluation involving ten methods, three of them proposed by the authors, to deal with the class imbalance problem in thirteen UCI data sets. Our experiments provide evidence that class imbalance does not systematically hinder the performance of learning systems. In fact, the problem seems to be related to learning with too few minority class examples in the presence of other complicating factors, such as class overlapping. Two of our proposed methods deal with these conditions directly, allying a known over-sampling method with data cleaning methods in order to produce better-defined class clusters. Our comparative experiments show that, in general, over-sampling methods provide more accurate results than under-sampling methods considering the area under the ROC curve (AUC). This result seems to contradict results previously published in the literature. Two of our proposed methods, Smote + Tomek and Smote + ENN, presented very good results for data sets with a small number of positive examples. Moreover, Random over-sampling, a very simple over-sampling method, is very competitive to more complex over-sampling methods. Since the over-sampling methods provided very good performance results, we also measured the syntactic complexity of the decision trees induced from over-sampled data. Our results show that these trees are usually more complex then the ones induced from original data. Random over-sampling usually produced the smallest increase in the mean number of induced rules and Smote + ENN the smallest increase in the mean number of conditions per rule, when compared among the investigated over-sampling methods. <s> BIB004 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes. <s> BIB005 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems. <s> BIB006 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> In this paper we present a complex hybrid model in the water management field based on a synergetic combination of deterministic and machine learning model components. The objective of a Water Supply System (WSS) is to convey treated water to consumers through a pressurized network of pipes. A number of meters and gauges are used to take continuous or periodic measurements that are sent via a telemetry system to the control and operation center and used to monitor the network. Using this typically limited number of measures together with demand predictions the state of the system must be assessed. Suitable state estimation is of paramount importance in diagnosing leaks and other faults and anomalies in WSS. But this task can be really cumbersome, if not unachievable, for human operators. The aim of this paper is to explore the possibility for a technique borrowed from machine learning, specifically a neuro-fuzzy approach, to perform such a task. For one thing, state estimation of a network is performed by using optimization techniques that minimize the discrepancies between the measures taken by telemetry and the values produced by the mathematical model of the network, which tries to reconcile all the available information. But, for another, although the model can be completely accurate, the estimation is based on data containing non-negligible levels of uncertainty, which definitely influences the precision of the estimated states. The quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate estimation. By making use of the mathematical model of the network, estimated states together with uncertainty levels, that is to say, fuzzy estimated states, for different anomalous states of the network can be obtained. These two steps rely on a theory-driven model. The final aim is to train a neural network (using the fuzzy estimated states together with a description of the associated anomaly) capable of assessing WSS anomalies associated with particular sets of measurements received by telemetry and demand predictions. This is the data-driven counterpart of the hybrid model. <s> BIB007 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced <s> BIB008 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> In the field of classification problems, we often encounter classes with a very different percentage of patterns between them, classes with a high pattern percentage and classes with a low pattern percentage. These problems receive the name of ''classification problems with imbalanced data-sets''. In this paper we study the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets, focusing on the synergy with the preprocessing mechanisms of instances and the configuration of fuzzy rule based classification systems. We will analyse the necessity of applying a preprocessing step to deal with the problem of imbalanced data-sets. Regarding the components of the fuzzy rule base classification system, we are interested in the granularity of the fuzzy partitions, the use of distinct conjunction operators, the application of some approaches to compute the rule weights and the use of different fuzzy reasoning methods. <s> BIB009 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> Urban water supply is a high priority service and so looped networks are extensively used in order to considerably reduce the number of consumers affected by a failure. Looped networks may be redundant in connectivity and capacity. The concept of reliability has been introduced in an attempt to quantitatively measure the possibility of maintaining an adequate service for a given period. Numerous researchers have considered reliability as a measure of redundancy. This concept is usually implicit, but some researchers have even stated it explicitly. This paper shows why reliability cannot be considered a measure of redundancy given that branched networks can achieve high values of reliability and this would deny the fact that a looped network is more reliable than a branched network with a similar layout and size. To this end the paper discusses two quantitative indices for measuring expected network behavior: reliability and tolerance. These indices are calculated and a comparison is made between looped, branched, and mixed networks. <s> BIB010 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of"normal"examples with only a small percentage of"abnormal"or"interesting"examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy. <s> BIB011 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Transformations associated to imbalanced <s> For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a "budget-sensitive" progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance. <s> BIB012
datasets As said before, some data mining methods require balanced datasets. However, imbalanced sets appear in many real applications. They appear where the number of instances of one subpopulation (or class, usually the general class representing the usual scenario) is much bigger than the number of instances of some other classes (usually abnormal classes), which frequently are the most informative and valuable ones. These often are the object of interest in the specific problem. Among others, imbalanced datasets typically appear in the identification of anomalies in certain systems designed to work under steady conditions. Some examples are: Water Distribution Systems BIB007 , the detection of oil spills from satellite images BIB003 , the identification of power distribution fault causes BIB008 and the prediction of pre-term births BIB002 . This issue is growing in importance since it appears more and more in most real domains, especially in systems where data from the usual scenario are abundant while data from abnormal ones are scarce. Most classical inductive machine learning algorithms generally perform poorly on imbalanced datasets, because they are designed to minimize the global error rate BIB001 , thus biasing toward the majority class. Classification algorithms tend to classify almost all instances within the majority class, poorly classifying the minority class. Using normal and abnormal data in a water distribution system as they are produced, without discrimination, produces a tolerance to failure unable to ensure capacity redundancy, and network operation under the failure of one component cannot be ensured BIB010 . To deal with imbalanced datasets several approaches are suitable: • Pre-processing: trying to balance data by oversampling the minority classes, under-sampling the majority one or a combination of both BIB004 BIB011 BIB005 . Resampling for building several balanced subsets of data and using ensemble methods policies are also used in these cases, to gain robustness. Also, introduction of synthetic data for abnormal situations, simulated on the basis of the real one is also used by some authors to balance the dataset and proceed normally. As an example, in BIB007  it is shown that over-sampling the abnormal class might produce an unacceptable rate of false positives that might carefully be analyzed. • Solutions at the algorithmic level: modifying the cost per class BIB006 , adjusting the probability estimation by establishing a bias towards the minority classes BIB012 , etc. In BIB009 , different pre-processing mechanisms are used in conjunction with a Fuzzy Rule Classification System to deal with imbalanced datasets.