We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis (ICA). Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to cl...

A survey study about the various methods of feature recognition with machine learning for affective computing is examined. In order to explore the methods of feature recognition with machine learning methods, Sequential Floating Forward Selection (SFFS), Minimum Redundancy – Maximum Relevance (mRMR), Information Gain(IG), and Fisher projection (FP) are discussed. As the machine learning methods...

In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support ...

In this paper, a decision tree-based approach for recognizing stem and calyx regions of apples by computer vision is proposed. The method starts with background removal and object segmentation by thresholding. Statistical, textural and shape features are extracted from each segmented object and these features are introduced to two decision tree algorithms: CART and C4.5. Feature selection is ac...

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an impro...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling ...

Experiment 2: TCS images were obtained by Philips SONOS 5500 with different examiners. Totally, 67 images from 38 PD patients and 71 images from 39 healthy subjects were included. All 77 subjects underwent a neurological examination. The rotationinvariant Gabor filter bank was applied to the ROI of TCS images. Then the Gabor features, mean, std and entropy, were calculated and evaluated by SVMs...

Feature selection is an importance step in classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, redundant, irrelevant data, and optimize the classification performance. This paper addresses a new subset feature selection performed by a new Social Spider Optimizer algorithm (SSOA) to find optimal regions of th...