Feature selection based on a fuzzy complementary criterion: application to gait recognition using ground reaction forces

A simple and efficient filter feature selection method via document-term matrix unitization

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

In this paper, PCA based modeling of geometric structure of the face for automatic face detection is presented. The method improves the face detection rate and limits the search space. Skin Color Modeling (SCM) is one of the best face detection techniques for image and video. However, feature selection is very important for even better template matching performance in terms of detection rate and time. This paper presents an efficient feature extraction and selection method based on geometric structure of the facial image boundary and interior. To model the geometric structure of face, Principle Component Analysis (PCA) and canny edge detection are used. Fusion of PCA based geometric modeling and SCM method provides higher face detection accuracy and improves time complexity. Both models provide filtering of image in term of pixel values to get the face location that are very fast and efficient for large image databases.

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.

Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.

QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods

Measurements of multi-axial Ground Reaction Force (GRF) can provides quite useful information for monitoring abnormalities of gait during Activities in Daily Living (ADL). Measuring multi-axial GRF using ambulatory GRF sensing systems is difficult due to the height of sensor and the number of sensors used. Therefore, we developed a wireless GRF sensing system having the low height (12 mm) and the capability of sensing simultaneously lateral and vertical GRF using only four optical sensors. The proposed biaxial force sensor showed the low interference rate (less than 0.1 %) and excellent repeatability (0.6 %). We extracted the lateral and vertical GRFs via Bluetooth communication during normal and pronated/supinated walking and presented a three dimensional vector diagram employing center of pressure and GRFs to provide visualization of the ratio between the lateral and vertical GRFs according to the amount of foot pronation. In healthy subjects, the lateral forces were observed less than 10 % of their bodyweights, while large lateral forces were observed in over pronated/supinated walking up to 20 %.

• Neural Network feature selection methodology for both numerical and categorical data. • Embedded feature selection provides model and feature subset in a single training. • Novel feature embedding for dealing with high-cardinality categorical features. • Validation of the methodology on several open and real industry dataset. • Comparison with several state-of-the-art feature selection methodologie. In an era of effortless data collection, the impact of machine learning — especially neural networks (NNs) — is undeniable. As datasets grow in size and complexity, efficiently handling mixed data types, including categorical and numerical features, becomes critical. Feature encoding and selection play a key role in improving NN performance, efficiency, interpretability, and generalisation. This paper presents GLEm-Net (Grouped Lasso with Embeddings Network), a novel NN-based approach that seamlessly integrates feature encoding and selection directly into the training process. GLEm-Net uses embedding layers to process categorical features with high cardinality, simplifying the model and improving generalisation. By extending the grouped Lasso regularisation to explicitly consider categorical features, GLEm-Net automatically identifies the most relevant features during training and returns them to the analyst. We evaluate GLEm-Net on open and proprietary industry datasets and compare it to state-of-the-art feature selection methodologies. Results show that GLEm-Net adapts to each dataset by allowing the NN to directly select subsets of most important features, offering on par performance with the best state-of-the-art feature selection methods, while eliminating the need for the external feature encoding and selection steps that are now incorporated in the NN training stage.

The aim of this study was to look at the maturational profile of gait parameters by measuring ground reaction forces during independent walking in children. Fifty-four normal children aged 1 to 5 years were examined. The children walked with eight force transducers under each sole. Gait velocity and step length increased with age, whereas step frequency remained relatively constant. Phases of double ground contact expressed as percentages of the total gait cycle decreased significantly from age 1 to 5 with the steepest decrease occurring in the first year of independent walking. No asymmetry between left and right could be detected for any of these parameters. The pattern of ground reaction forces with a significant heel strike and obvious enrollment process resembling that in adults was achieved between the age of 2 and 3 years. Measuring ground reaction forces is a fast and easily manageable method of analysing gait pattern in children and is also a promising tool for detection of gait abnormalities in children with neurological disease.

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise.

In walking analysis, which is one useful method for efficient physical rehabilitation, the ground reaction force, the center of pressure, and the body orientation data are measured during walking. In the past, these data were measured by a 3D motion analysis system consisting of high-speed cameras and force plates, which must be installed in the floor. However, a conventional 3D motion analysis system can measure the ground reaction force and the center of pressure just on force plates during a few steps. In addition, the subjects' stride lengths are limited because they have to walk on the center of the force plate. These problems can be resolved by converting conventional devices into wearable devices. We used a measuring device consisting of portable force plates and motion sensors. We developed a walking analysis system that calculates the ground reaction force, the center of pressure, and the body orientations and measured a walking subject to estimate this system. We simultaneously used a conventional 3D motion analysis system to compare with our development system and showed its validity for measurements of ground reaction force and the center of pressure.

Gorillas and chimpanzees both use knuckle-walking (KW). Whether this behavior is a homologous trait inherited from their last common ancestor or the result of convergent evolution remains debated. This study investigated the biomechanics of gorilla KW by measuring ground reaction forces (GRFs) and comparing them with previously reported chimpanzee data. GRFs were recorded using two in-series force plates embedded in a horizontal beam in the outdoor enclosure of Kyoto City Zoo. Three adult gorillas voluntarily performed KW during normal activity, and 90 steady-state trials were analyzed. The results revealed interspecific differences: chimpanzees exhibited greater peak vertical GRFs in the hindlimbs, approximately 1.5 times those in the forelimbs, suggesting greater hindlimb weight support. In contrast, gorillas showed comparable peak GRFs in both limbs, with slightly higher values in the hindlimbs, indicating a more balanced weight distribution. Moreover, two individuals showed out-of-phase fluctuations in gravitational and kinetic energy of the center of mass, indicating an inverted pendulum mechanism and relatively efficient energy recovery. The distinct GRF patterns and apparent mechanical efficiency suggest that gorilla KW differs biomechanically from that of chimpanzees. These findings appear to be consistent with the hypothesis that KW in gorillas and chimpanzees evolved independently, which may suggest convergent evolution rather than shared ancestry.

The measurement of ground reaction force (GRF) is important in biomechanical analysis for clinical gait assessment and gait research. The GRF measured by the force plate, along with the gravity, are the only significant external forces acting on the body during gait. The purpose of this study is to show the effects of walking speed on vertical ground reaction force (VGRF) F z using Kistler quartz force platform embedded on the floor. Since considerable differences in the gait of normal subjects arises due to ‘constrained’ walking, so subject are made to walk with their self selected walking speed in the walkway. Vertical component of the GRF was analysed from the recorded data using Bioware software. It was clearly observed that with increasing walking speed VGRF increased at F z 1 and F z 3 whereas decreased at F z 2 .

The paper proposes a hybrid approach to reduce dimension in text classification problems, to overcome the issue of Curse of Dimensionality. This hybrid approach is a combination of Feature Selection (FS) and Feature Extraction (FE) methods, considering different aspects of feature relevance, to effectively reduce the dimension in large text datasets. It prevents feature selection biased in favor of a particular FS method. Many FS methods like Term Variance, Document Frequency, Information Gain, Shannons Entropy measure, Mean-Median and Mean Absolute Difference, were implemented and a comparative study was made on their performance when implemented in a hybrid system. The features selected by the individual FS methods are merged using three approaches, namely, Union, Intersection and Modified Union. The sub lists of features further undergo Feature Extraction by PCA, and the reduced feature sub list is clustered with k-means. Finally, the sentiment-score of the individual clusters are calculated using SentiWordNet database which gives the polarity of the data. The experiments were conducted on the benchmark datasets namely Reuters-21,578 and Classic4. The performance evaluation of the system made using the measures like precision, recall, f-score and accuracy shows that the proposed method has improved performance compared to its competitive methods.

Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a bi-objective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other complementary features can greatly improve the classification performance. Thus, it is necessary to consider not only the performance of feature subsets in the objective space, but also their differences in the search space, to explore more promising feature combinations. To this end, this paper proposes a tri-objective method for bi-objective feature selection in classification, which solves a bi-objective feature selection problem as a tri-objective problem by considering the diversity (differences) between feature subsets in the search space as the third objective. The selection based on the converted tri-objective method can maintain a balance between minimizing the number of selected features, maximizing the classification performance, and exploring more promising feature subsets. Furthermore, a novel initialization strategy and an offspring reproduction operator are proposed to promote the diversity of feature subsets in the objective space and improve the search ability, respectively. The proposed algorithm is compared with five multiobjective-based feature selection methods, six typical feature selection methods, and two peer methods with diversity as a helper objective. Experimental results on 20 real-world classification datasets suggest that the proposed method outperforms the compared methods in most scenarios.