An Embedded Feature Selection Method for Imbalanced Data Classification

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index(WGI) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve(ROC AUC) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20% or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

Analysis of Evolutionary Social Media Activities: Pre-Vaccine and Post-Vaccine Emergency Use

Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022.It continues to have a negative impact on human daily life and the global economic development till now,due to the lack of effective treatment of COVID-19 induced issues and prevention of transmission methods.

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'.

Negatively charged organic–inorganic hybrid silica nanofiltration membranes for lithium extraction

Effective extraction of lithium from high Mg2+/Li+ratio brine lakes is of great challenge.In this work,organic–inorganic hybrid silica nanofiltration(NF)membranes were prepared by dip-coating a 1,2-bis(triethoxysilyl)ethane(BTESE)-derived separation layer on tubular TiO2 support,for efficient separation of LiC l and MgCl2 salt solutions.We found that the membrane calcinated at 400°C(M1–400)could exhibit a narrow pore size distribution(0.63–1.66 nm)owing to the dehydroxylation and the thermal degradation of the organic bridge groups.All as-prepared membranes exhibited higher rejections to LiCl than to MgCl2,which was attributed to the negative charge of the membrane surfaces.The rejection for LiCl and MgCl2 followed the order:LiCl N MgCl2,revealing that Donnan exclusion effect dominated the salt rejection mechanism.In addition,the triplecoated membrane calcined at 400°C(M3–400)exhibited a permeability of about 9.5 L·m-2·h-1·bar-1 for LiCl or MgCl2 solutions,with rejections of 74.7%and 20.3%to LiCl and MgCl2,respectively,under the transmembrane pressure at 6 bar.Compared with the previously reported performance of NF membranes for Mg2+/Li+separation,the overall performance of M3–400 is highly competitive.Therefore,this work may provide new insight into designing robust silica-based ceramic NF membranes with negative charge for efficient lithium extraction from salt lakes.

紫花针茅根际和体内真菌群落结构及与土壤环境因子的相关性研究

【目的】探究青藏高原不同地区高寒草原紫花针茅根际和体内真菌群落的组成、多样性等特征,及与土壤环境因子(理化性质和酶活性)间的相互关系。【方法】从青藏高原不同地区采集紫花针茅样品,应用土壤化学方法分析根际土壤理化性质和酶活性,并采用Illumina Miseq高通量测序技术,解析根际土壤和体内真菌群落组成和丰度、Alpha多样性和菌群结构,同时分析了紫花针茅根际真菌种群多样性与土壤环境因子的相关性,厘清了影响紫花针茅根际真菌区系的土壤环境因素。【结果】三个采样地的根际土壤呈中性偏碱,土壤理化性质和酶活性变化各异。高通量测序共得到314801条有效序列和4491个OTUs;XZ样地的紫花针茅真菌多样性和丰富度相对偏低,GS样地最高。在门分类水平上,子囊菌门Ascomycota和担子菌门Basidiomycota是主要内生真菌类群,占总菌群的88.28%。不同采样地区紫花针茅体内真菌群落结构存在明显差异,而根际土壤真菌群落结构差异不大。相关性分析表明,紫花针茅真菌多样性与土壤pH、有效钾、铁、钙、镁、多酚氧化酶、过氧化物酶和脱氢酶呈显著(P<0.05)或极显著(P<0.01)正相关,而与海拔、土壤酸性磷酸酶呈极显著负相关。RDA分析发现,紫花针茅根际土壤真菌不同,影响的土壤环境因子也不同。【结论】青藏高原高寒草地紫花针茅根际和体内栖息着丰富的真菌群落,其组成和多样性受多种土壤环境因子影响,且影响不同真菌群落的主要土壤环境因子也不同。本研究对于有益微生物资源的开发、利用及保护具有重要意义,并为紫花针茅草原保育和合理开发利用提供科学依据。

数字化转型对企业价值的影响研究

本文以2009~2022 A股上市企业为研究样本,通过构建双向固定效应模型,考察了数字化转型对企业价值的影响,探讨了销售收入在这一过程中的部分中介效应,并结合具体企业的时间序列分析预测,论证了数字化转型对企业价值影响的长期性。研究发现:数字化转型通过提高企业销售收入进而促进企业价值的提升;异质性检验表明,数字化转型对企业价值的正向影响在非国有、两职分离企业中更为明显。本文为评估企业数字化转型对实体经济增长的转化提供了理论支撑,对发挥数据要素作用以促进企业供给侧结构性改革具有一定参考价值。