An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.<br /

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

GaitGCI: Generative Counterfactual Intervention for Gait Recognition

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild).Comment: Accepted by CVPR202

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.<br /

Performance analysis of photonic RF self-interference cancellation for full-duplex communication

A photonic RF self-interference cancellation (SIC) scheme for full-duplex communication is proposed and demonstrated experimentally. It is based on phase modulation to convert the RF signal into optical domain. The interference cancellation performance of the photonic RF SIC system under different delay deviation (Δτ) and amplitude deviation (Δα) is analyzed. The cancellation depth of 34.5 dB is measured for 10 GHz signal with bandwidth of 50MHz. According to experimental results, the interference cancellation performance affected by the time delay deviation, the amplitude deviation and the phase response is investigated. The results give a direction for the improvement of system performance

Exploring Browsing Behavior of Product Information in an M-commerce Application: a Transaction Log Analysis

This research aims to describe the information browsing and merchandise purchasing behaviors of the users in an M-commerce application. Data used in this research comes from the transaction logs of 290 heavy users in March 2015. We established the mapping between the request parameters in the log and the user information behavior to future analyze the pattern of user behavior. People are most concerned about the details of items, and actively share their favorite items and shops to others. The times of view is power-law distribution. We also find that the items which are viewed 9 times and are included in the submitted order are most likely to be bought. There is a positive correlation between the purchase of items and the numbers of browsing and sharing behaviors

A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.<br /

LIDAR GAIT: Benchmarking 3D Gait Recognition with Point Clouds

Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. In this work, instead of extracting gait features from images, we explore precise 3D gait features from point clouds and propose a simple yet efficient 3D gait recognition framework, termed multi-view projection network (MVPNet). MVPNet first projects point clouds into multiple depth maps from different perspectives, and then fuse depth images together, to learn the compact representation with 3D geometry information. Due to the lack of point cloud datasets, we build the first large-scale Lidar-based gait recognition dataset, LIDAR GAIT, collected by a Lidar sensor and an RGB camera mounted on a robot. The dataset contains 25,279 sequences from 1,050 subjects and covers many different variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that, (1) 3D structure information serves as a significant feature for gait recognition. (2) MVPNet not only competes with five representative point-based methods, but it also outperforms existing camera-based methods by large margins. (3) The Lidar sensor is superior to the RGB camera for gait recognition in the wild. LIDAR GAIT dataset and MVPNet code will be publicly available.Comment: 16 pages, 16 figures, 3 table

Users’ Emotional Experiences during Interaction with Information Products: A Diary Study

Emotional experience is a very important aspect of users’ interaction with information products. Previous research has agreed that emotion is an important ingredient which could enhance the interaction between human and computer. In this paper, we explored users’ emotional experience in relationships with other factors such as product types, product features, interaction results, and user behaviors. We analyzed 162 dairy entries from 36 users in 2 weeks. Results show that: (1) users recorded more negative emotions than positive emotions; (2) mobile apps were related to more positive emotions while desktop software was related to more negative emotions; (3) there is no significant correlation between user behaviors and emotions. The results provide exploratory understanding of the relationships between emotional experience and other factors. We propose that users’ expectation might play a key role in this process

Stop sending me messages!: The negative impact of persuasive messages on green transportation

Persuasive information and communication technology has been used to persuade people to choose fuel-efficient transportation (i.e., green transportation), for example, by sending messages to the public. Many factors may influence the effect of such messages. In this exploratory we report a social experiment, in which participants received persuasive messages from social and non-social approaches. To our surprise, results seem to show a negative impact on green transportation, meaning participants receiving the messages used less green transportation modes. This suggests that messages may not be as an effective way to persuade the public as many organizations’ practice assumes and other persuasive techniques such as real-time feedback and awareness raising techniques may be needed in causing the desired changes