Feature selection (FS) is a fundamental problem in the field of pattern recognition, which aims to find a minimal feature subset from the original feature space while retaining a suitably high accuracy in representing the original features. FS is used to improve the efficiency of learning algorithm especially for large scale datasets, by finding a minimal subset of features that has maximum eff...

In this paper we present an improved Sequential Forward Floating Search algorithm. Subsequently, extensive tests are carried out on a selection of French emotional language resources well suited for a first impression on general applicability. A detailed analysis is presented to test the various modifications suggested one-by-one. Our conclusion is that the modification in the forward step resu...

This paper proposes a genetic algorithm feature selection (GAFS) for image retrieval systems and image classification. Two texture features of adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM) and color feature of an adaptive color histogram for K-means (ACH) were used in this paper. In this paper, the feature selections have adopted sequential forwa...

We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using...

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the ...

Recently, the problem of signal representation in terms of basis vectors from a large, ”overcomplete”, spanning dictionary has been the focus of much research. Achieving a succinct, or ”sparse”, representation is known as the problem of best basis representation. We consider methods which seek to solve this problem by sequentially building up a basis set for the signal. Three distinct algorithm...

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...

A relevance filter is proposed which removes features based on the mutual information between class labels and features. It is proven that both feature independence and class conditional feature independence are required for the filter to be statistically optimal. This could be shown by establishing a relationship with the conditional relative entropy framework for feature selection. Removing f...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

A major characteristic of text document classification problem is extremely high dimensionality of text data. In this paper we present two algorithms for feature (word) selection for the purpose of text classification. We used sequential forward selection methods based on improved mutual information introduced by Battiti [1] and Kwak and Choi [6] for non-textual data. These feature evaluation f...