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This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
K nearest neighbor algorithm (K-NN) is considered as one of the machine learning algorithms for data classification. This algorithm suffers of some disadvantages such as sensitivity to the distance function, K value selection and high computational complexity (time and spatial). In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting with k value...
In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature...
Data preprocessing is an important data manipulation process prior to mining actions. Various techniques that include feature selection and data transformation have been studied in the past, with the aim of producing a compact and efficient decision tree. They all have their respective strengths, but in general they commonly lack of preserving the meanings of the attributes. The concept of Attribute...
Data-related issues represent the main causes for insufficient performance in data mining. Existing strategies for tackling these issues include procedures for handling incomplete data - mandatory in various schemes, and feature selection, both augmenting the learning process. Our previous work on data imputation has shown that a good imputation policy for strongly correlated attributes with the class...
Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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