Feature selection and case organization are crucial steps in case-based reasoning (CBR), since the retrieval efficiency and accuracy even the success of the CBR system are heavily dependent on their quality. However, inappropriate feature selection and case selection together with ill-structured case organization may not only present a dilemma in case retrieval, but also greatly increase the ca...

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain r...

In recent years, with the development of Chinese semantically annotated corpus, such as Chinese Proposition Bank and Normalization Bank, the Chinese semantic role labeling (SRL) task has been boosted. Similar to English, the Chinese SRL can be divided into two tasks: semantic role identification (SRI) and classification (SRC). Many features were introduced into these tasks and promising results...

In this paper, we propose a real-time system, Hierarchical Feature Selection (HFS), that performs image segmentation at a speed of 50 frames-per-second. We make an attempt to improve the performance of previous image segmentation systems by focusing on two aspects: (1) a careful system implementation on modern GPUs for efficient feature computation; and (2) an effective hierarchical feature sel...

Placing sensors in every station of a process or every element of a system to monitor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multi-stream signals from a distributed sensing system, but should als...

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker dis...

This paper proposes a novel hierarchical content-based image retrieval system and its application to skin lesion images. Five common classes of skin lesions, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Feature selection is embedded in a hierarchical framework that chooses the most relevant feature subsets by comparing different simi...

In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve accuracy, both of which are commonly treated as two objectives in FS problems. Many meta-heuristic algorithms have been applied to solve problems they perform satisfactorily when problem is relatively simple. However, once datasets grows, their performance drops dramatically. This paper propo...

There are several compilations of sky classifications that refer to Meteorological Indices (MIs) (variables usually recorded at meteorological ground stations), due the scarcity scanner devices can supply experimental data needed apply CIE standard classification. The use one rather than another MI is never justified, because there no standardized criterion for their selection. In this study, f...