In terms of the travel demand prediction from household car ownership model, if imbalanced data were used to support transportation policy via a machine learning it would negatively affect algorithm training process. The on obtained study project for expressway preparation in Khon Kaen Province (2015) was an unbalanced dataset. other words, number members minority class is lower than rest answe...

Predictive models are often used for real-time decision making. However, typical machine learning techniques ignore feature evaluation cost, and focus solely on the accuracy of the machine learning models obtained utilizing all the features available. We develop algorithms and indexes to support cost-sensitive prediction, i.e., making decisions using machine learning models taking feature evalu...

The access to inapropiate Internet content is an increasing problem that can be approached as a cost-sensitive Automated Text Categorization task. In this paper, we report a series of experiments that compare a representative range of learning algorithms and methods for making them cost-sensitive, on two Web pages collections in Spanish and English. The results of our experiments are promising.

Intrusion detection systems (IDSs) must maximize the realization of security goals while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models. We examine the major cost factors associated with an IDS, which include development cost, operational cost, damage cost due to successful intrusions, and the cost of manual and automated response to ...

A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to costsensitive classification, i...

Sensitive error correcting output codes are a reduction from cost sensitive classi cation to binary classi cation. They are a modi cation of error correcting output codes [3] which satisfy an additional property: regret for binary classi cation implies at most 2 l2 regret for cost-estimation. This has several implications: 1) Any 0/1 regret minimizing online algorithm is (via the reduction) a r...

The classification of data with imbalanced class distributions has posed a significant drawback in the performance attainable by most well-developed classification systems, which assume relatively balanced class distributions. This problem is especially crucial in many application domains, such as medical diagnosis, fraud detection, network intrusion, etc., which are of great importance in mach...

We present new global and local policy search algorithms suitable for problems with policy-dependent cost variance (or risk), a property present in many robot control tasks. These algorithms exploit new techniques in nonparameteric heteroscedastic regression to directly model the policy-dependent distribution of cost. For local search, the learned cost model can be used as a critic for performi...

Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can b...