In this paper we perform an hierarchical clustering in high – dimensional spaces, without first applying any space reduction. Instead, in each step of the algorithm we perform a soft feature selection, witch does not have to be shared among all input elements. The main goal is to correctly identify the patterns that underly in the data. The proposed algorithm is applied, with promising results,...

Feature selection is an important task in data mining and pattern recognition, especially for high-dimensional data. It aims to select a compact feature subset with the maximal discriminative capability. The discriminability of a feature subset requires that selected features have a high relevance to class labels, whereas the compactness demands a low redundancy within the selected feature subs...

The number of messages that can be mined from online entries increases as the number of online application users increases. In Malaysia, online messages are written in mixed languages known as ‘Bahasa Rojak’. Therefore, mining opinion using natural language processing activities is difficult. This study introduces a Malay Mixed Text Normalization Approach (MyTNA) and a feature selection techniq...

This paper presents a fully scalable image coding scheme based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The proposed algorithm, called Fully Scalable SPIHT (FS-SPIHT), adds the spatial scalability feature to the SPIHT algorithm. It provides this new functionality without sacrificing other important features of the original SPIHT bitstream such as: compression efficiency,...

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for many mining and learning tasks. Features of real-world high-dimensional data such as words of documents, pixels of images and genes of microarray data, usually present inherent hierarchical structures. In a hierarchical structure, features could share certain properties. Such information has b...

Abstract Since the number of features dataset is much higher than patterns, dimension data, greater impact on learning algorithm. Dimension disaster has become an important problem. Feature selection can effectively reduce and improve performance Thus, in this paper, A feature algorithm based P systems (P-FS) proposed to exploit parallel ability cell-like advantage evolutionary algorithms searc...

Feature selection (FS) is an important preprocessing step in machine learning and data mining. In this paper, a new feature subset evaluation method is proposed by constructing a sample graph (SG) in different k-features and applying community modularity to select highly informative features as a group. However, these features may not be relevant as an individual. Furthermore, relevant in-depen...

Multiple kernel learning for feature selection (MKLFS) utilizes kernels to explore complex properties of features and performs better in embedded methods. However, the kernels in MKL-FS are generally limited to be positive definite. In fact, indefinite kernels often emerge in actual applications and can achieve better empirical performance. But due to the non-convexity of indefinite kernels, ex...

The main contribution of this paper is to present a novel approach for classifying the sleep stages based on optimal feature selection with ensemble learning stacking model using single-channel EEG signals.To find suitable features from extracted vector, we obtained ReliefF (ReF), Fisher Score (FS) and Online Stream Feature Selection (OSFS) algorithms.The proposed research work was performed tw...