The National Police Service Agency of the Netherlands developed a model to classify (potential) jihadists in four sequential phases of radicalism. The goal of the model is to signal the potential jihadist as early as possible to prevent him or her to enter the next phase. This model has up till now, never been used to actively find new subjects. In this paper, we use Formal Concept Analysis to extract and visualize potential jihadists in the different phases of radicalism from a large set of reports describing police observations. We employ Temporal Concept Analysis to visualize how a possible jihadist radicalizes over time. The combination of these instruments allows for easy decision-making on where and when to act.

In this article, we investigate the issue of cost-sensitive classification for a data set of Massachusetts closed personal injury protection (PIP) automobile insurance claims that were previously investigated for suspicion of fraud by domain experts and for which we obtained cost information. After a theoretical exposition on cost-sensitive learning and decision-making methods, we then apply these methods to the claims data at hand to contrast the predictive performance of the documented methods for a selection of decision tree and rule learners. We use standard logistic regression and (smoothed) naive Bayes as benchmarks. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1197–1215, 2004.

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.

We apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) to the problem of diagnosing insurance claim fraud. The method effectively combines the advantages of boosting and the explanatory power of the weight of evidence scoring framework. We present the results of an experimental evaluation with an emphasis on discriminatory power, ranking ability, and calibration of probability estimates. The data to which we apply the method consists of closed personal injury protection (PIP) automobile insurance claims from accidents that occurred in Massachusetts (USA) during 1993 and were previously investigated for suspicion of fraud by domain experts. The data mimic the most commonly occurring data configuration, that is, claim records consisting of information pertaining to several binary fraud indicators. The findings of the study reveal the method to be a valuable contribution to the design of intelligible, accountable, and efficient fraud detection support.

We study the problem of repeat-purchase modeling in a direct marketing setting using Belgian data. More specifically, we investigate the detection and qualification of the most relevant explanatory variables for predicting purchase incidence. The analysis is based on a wrapped form of input selection using a sensitivity based pruning heuristic to guide a greedy, stepwise, and backward traversal of the input space. For this purpose, we make use of a powerful and promising least squares support vector machine (LS-SVM) classifier formulation. This study extends beyond the standard recency frequency monetary (RFM) modeling semantics in two ways: (1) by including alternative operationalizations of the RFM variables, and (2) by adding several other (non-RFM) predictors. Results indicate that elimination of redundant/irrelevant inputs allows significant reduction of model complexity. The empirical findings also highlight the importance of frequency and monetary variables, while the recency variable category seems to be of somewhat lesser importance to the case at hand. Results also point to the added value of including non-RFM variables for improving customer profiling. More specifically, customer/company interaction, measured using indicators of information requests and complaints, and merchandise returns provide additional predictive power to purchase incidence modeling for database marketing. © 2001 John Wiley & Sons, Inc.

We discuss the implementation of a wrapped neural network feature selection approach, introduced here as the weight cascaded retraining (WCR) algorithm. The paper provides an outline of the algorithm and elaborates on its formal underpinnings. Central to the whole feature pruning approach is the iteratively conceived guided function optimisation realised by passing the optimised weight vector from one iteration step to the next. This essentially gives rise to a cascaded form of neural network retraining. The theoretical exposition of the WCR algorithm is illuminated and benchmarked by means of the publicly available UCI case material. It is illustrated that WCR based neural network feature selection may be very effective in reducing model complexity for classification modelling via neural networks.

Recently, a modified version of support vector machines (SVMs), least-squares SVM (LS-SVM) classifiers, has been introduced, which is closely related to a form of ridge regression-type SVMs. In LS-SVMs, the classifier is obtained as the solution to a linear system instead of a quadratic programming problem. In this paper, UCI (University of California at Irvine) benchmark data sets are used to evaluate the performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. The hyperparameters of the LS-SVM problem formulation are tuned using a 10-fold cross-validation procedure and a grid search mechanism. When comparing the performance of a nonlinear (RBF or polynomial) LS-SVM classifier with that of a linear LS-SVM, additional insight can be gained into the degree of nonlinearity of the classification problem at hand. Using a statistical motivation, it is concluded that RBF LS-SVM classifiers consistently yield among the best results for each data set.

In object-oriented conceptual modeling, the generalization/specialization hierarchy and the whole/part relationship are prevalent classification schemes for object types. This paper presents an object-oriented conceptual model where, in the end, object types are classified according to two relationships only. Existence dependency and generalization/specialization. Existence dependency captures some of the interesting semantics that are usually associated with the concept of aggregation (also called composition or Part Of relation), but in contrast with the latter concept, the semantics of existence dependency are very precise and its use clear cut. The key advantage of classifying object types according to existence dependency are the simplicity of the concept, its absolute unambiguity, and the fact that it enables to check conceptual schemes for semantic integrity and consistency. We will first define the notion of existence dependency and claim that it is always possible to classify objects according to this relationship, thus removing the necessity for the Part Of relation and other kinds of associations between object types. The second claim of this paper is that existence dependency is the key to semantic integrity checking to a level unknown to current object-oriented analysis methods. In other words: Existence dependency allows us to track and solve inconsistencies in an object-oriented conceptual schema.

The measure property set of Briand, Morasca, and Basili (1996) establishes the foundation of a real software measurement theory. Unfortunately, a number of inconsistencies related to additivity properties might hinder its acceptance and further elaboration. The authors show how to remove the ambiguity in the property definitions.