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This work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance...
Many machine learning algorithms require a single value per feature per record for modeling. However, there are applications, in the medical domain particularly, where a single record may have multiple observations for the same feature. For example, a patient could have the same gene analyzed in multiple tissue slides of a biopsy, or could have the same genetic test performed on multiple subsequent...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
This paper presents a novel approach to eliminate the effect of noisy samples from the learning step of support vector data description (SVDD) method. SVDD is a popular kernel method which tries to fit a hypersphere around the target object and can obtain more flexible and more accurate data descriptions by using proper kernel functions. Nonetheless, the SVDD could sometimes generate such a loose...
In recent years, anonymization methods have emerged as an important tool to preserve individual privacy when releasing privacy sensitive data sets. This interest in anonymization techniques has resulted in a plethora of methods for anonymizing data under different privacy and utility assumptions. At the same time, there has been little research addressing how to effectively use the anonymized data...
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