The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors. Data imbalance is an inherent problem in activity recognition caused by the irregular time distribution of activities, which are sequential and dependent on previous movements. We use conditional random fields (CRF), a graphical model for structured classification, to take advantage of dependencies between activities in a sequence. However, CRFs do not consider the negative effects of class imbalance during training. We propose a class-wise dynamically weighted CRF (dWCRF) where weights are automatically determined during training by maximizing the expected overall F-score. Our results based on three case studies from a healthcare application using a batteryless body worn sensor, demonstrate that our method, in general, improves overall and minority class F-score when compared to other CRF based classifiers and achieves similar or better overall and class-wise performance when compared to SVM based classifiers under conditions of limited training data. We also confirm the performance of our approach using an additional battery powered body worn sensor dataset, achieving similar results in cases of high class imbalance.
The study of @cite_24 , focused on improving the classification performance by modifying costs according to specific performance tasks (task-wise) such as improving recall, precision or both (as in ) @cite_24 as opposed to classification error minimization as in previous studies. The method itself did not consider class specific parameters and parameter calculations required an extensive validation process as parameters were not learned in training. The introduction of weights in CRF (WCRF) is not new; however, previous approaches only considered using a fixed set of weights during training for optimization @cite_30 ; however, finding an optimal set of weights @cite_24 @cite_25 require an extensive validation process.
@cite_24 @cite_25 @cite_30
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We describe a method of incorporating task-specific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduced in the speech recognition community, we describe the method generally for structured models, highlight connections to CLL and max-margin learning for structured prediction (, 2003), and show that the method optimizes a bound on risk. The approach is simple, efficient, and easy to implement, requiring very little change to an existing CLL implementation. We present experimental results comparing with several commonly-used methods for training structured predictors for named-entity recognition. Abstract Cost-sensitive learning has received increased attention in recent years. However, in existing studies, most of the works are devoted to make decision trees cost-sensitive and very few works discuss cost-sensitive Bayesian network classifiers. In this paper, an instance weighting method is incorporated into various Bayesian network classifiers. The probability estimation of Bayesian network classifiers is modified by the instance weighting method, which makes Bayesian network classifiers cost-sensitive. The experimental results on 36 UCI data sets show that when cost ratio is large, the cost-sensitive Bayesian network classifiers perform well in terms of the total misclassification costs and the number of high cost errors. When cost ratio is small, the advantage of cost-sensitive Bayesian network classifiers is not so obvious in terms of the total misclassification costs, but still obvious in terms of the number of high cost errors, compared to the original cost-insensitive Bayesian network classifiers.