Multi-level aircraft feature representation and selection for aviation environmental impact analysis

With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training's computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA's inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA's insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm.

This study evaluated the direct and indirect environmental impacts of various unit operations of a industrial waste incineration plant by using the life cycle assessment tool and reviewed the improvement plan. During the incineration process, the direct environmental impact was decreased with decrease in emission of various air pollutants by incorporating an air pollution prevention facilities. However, an increase in indirect environmental impacts was observed as a consequence of resources and energy of consumption at the various operational facilities. Consequently, quantitative direct and indirect impact were 89.1%, 10.9%, respectively. The environmental impact analysis of system revealed the highest impact of incineration followed by the impacts of other unit processes such as semidry reactor, and bag-filter. The various air pollutants and ashes generated during the incineration process caused the most significant environmental impact. Among the various categories of environmental impact, global warming accounted the highest impact(more than 85%) followed by eutrophication, and abiotic depletion. As a result of the avoided impact by the utilization of heat generated during the waste incineration process, using an incineration heat for steam and electricity obtained the impact reduction of 45.5%, 19.8%. So, during siting of new incineration plant, the utilization of steam generated from the waste combustion is highly considered to reduce the environmental impact.

Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to find a suitable EC method and corresponding suitable parameter values for a given large-scale feature selection problem if we want to solve it effectively and efficiently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for large-scale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classification accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems.

The problem of feature selection has been an area of considerable research in machine learning. Feature selection is known to be particularly difficult in unsupervised learning because different subgroups of features can yield useful insights into the same dataset. In other words, many theoretically-right answers may exist for the same problem. Furthermore, designing algorithms for unsupervised feature selection is technically harder than designing algorithms for supervised feature selection because unsupervised feature selection algorithms cannot be guided by class labels. As a result, previous work attempts to discover intrinsic structures of data with heavy computation such as matrix decomposition, and require significant time to find even a single solution. This paper proposes a novel algorithm, named Explainability-based Unsupervised Feature Value Selection (EUFVS), which enables a paradigm shift in feature selection, and solves all of these problems. EUFVS requires only a few tens of milliseconds for datasets with thousands of features and instances, allowing the generation of a large number of possible solutions and select the solution with the best fit. Another important advantage of EUFVS is that it selects feature values instead of features, which can better explain phenomena in data than features. EUFVS enables a paradigm shift in feature selection. This paper explains its theoretical advantage, and also shows its applications in real experiments. In our experiments with labeled datasets, EUFVS found feature value sets that explain labels, and also detected useful relationships between feature value sets not detectable from given class labels.KeywordsFeature selectionUnsupervised learningClustering

Feature Selection methods in Data Mining and Data Analysis problems aim at selecting a subset of the variables, or features, that describe the data in order to obtain a more essential and compact representation of the available information. The selected subset has to be small in size and must retain the information that is most useful for the specific application. The role of Feature Selection is particularly important when computationally expensive Data Mining tools are used, or when the data collection process is difficult or costly. Feature Selection problems are typically solved in the literature using search techniques, where the evaluation of a specific subset is accomplished by a proper function (filter methods) or directly by the performance of a Data Mining tool (wrapper methods). In this work we show how the Feature Selection problem can be formulated as a subgraph selection problem derived from the lightest k-subgraph problem, and solved as an Integer Program. The proposed formulation is very flexible, as additional conditions on the solution can be added in the formulation. Although optimal solutions for such problems are difficult to find in the worst case, a large number of test instances have been solved efficiently by commercial tools. Finally, an application to a database on urban mobility is presented, where the proposed method is integrated in the Data Mining tool named Lsquare and is compared with other approaches.

Economic feasibility and environmental impact analysis of landfill gas to energy technology in African urban areas

In many data analysis tasks, one is often confronted with very high dimensional data. Feature selection techniques are designed to find the relevant feature subset of the original features which can facilitate clustering, classification and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenario, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional unsupervised feature selection methods address this issue by selecting the top ranked features based on certain scores computed independently for each feature. These approaches neglect the possible correlation between different features and thus can not produce an optimal feature subset. Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, we propose in this paper a new approach, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection. Specifically, we select those features such that the multi-cluster structure of the data can be best preserved. The corresponding optimization problem can be efficiently solved since it only involves a sparse eigen-problem and a L1-regularized least squares problem. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm.

Feature selection is an important research issue in classification. As an effective global optimization technique, Particle Swarm Optimization (PSO) algorithm has been widely employed to solve feature selection problems. Population initialization is an important stage in Evolutionary Computation (EC) techniques. Therefore, in EC research field, many works focus on population initialization and it has been verified that suitable population initialization methods can significantly improve the performance of EC techniques. The efficient of PSO significantly deteriorates when being used to solve the large-scale feature selection problems. At present, only a few works have been done to enhance the performance of PSO for feature selection problems through population initialization. Meanwhile, there are many excellent traditional filter feature selection methods. Their characteristic is quick in speed but low in effect. However, they can provide a lot of useful heuristic information. Therefore, this paper focuses on improving the performance of PSO for feature selection problems by designing filter-based population initialization methods. In the proposed method, the filters are firstly used to evaluate features. Based on the obtained heuristic information, the initialization method combining the mixed initialization and the threshold selection is designed. The experiments are carried out on several datasets and the proposed initialization method is compared to some related initialization methods. Besides, the K-Nearest Neighbour (KNN) and other compatible fitness learners are used for performance assessment for feature selection. The results show that the proposed method using KNN classifier is promising to improve the performance of PSO for feature selection problems.

A simplified method for quantifying environmental impacts in the landscape planning/design process

To protect the environment, EU promulgated EUP directive which is more serious and complex than WEEE and RoHS directive. To help dishwasher manufacturers to follow the EUP directive well in China, the environmental impact of the dishwasher in the whole life cycle is systematically assessed based on CML method, and the environmental impact caused by detergent is not included. The impact on the environment caused by dishwasher is interpreted from resource consumption, pollutant emission and environmental impact. Finally, the work system of dishwasher is analysed, and the improvements measures are introduced.

Exergy-based environmental impact analysis in the industrial processes

In classic methodology of environmental evaluation emission data are taken into account as a basis for the environmental impact analysis. This means that location of the system and its components are not important. In other words environmental impact of the system placed in particular area could be identical in quite different area. The data about elements of environmental that could be affected by the system is usually omitted. It seems not to be the proper approach. If the installation is placed in populated area it should have much worse environmental score that the same installation placed on the desert. In order to modify the environmental impact assessment procedures and make it possible to include location as an important factor one should change emission approach into immision one. The sensitivity of the particular area should be defined in all impact categories — which should be describes in global and local terms. Some remarks about environmental impact analysis are placed in the paper as well as proposed modifications in EIA procedures in order to grasp location problems — taking as an example waste management system.

Feature selection plays an important role in data preprocessing. The aim of feature selection is to recognize and remove redundant or irrelevant features. The key issue is to use as few features as possible to achieve the lowest classification error rate. This paper formulates feature selection as a multi-objective problem. In order to address feature selection problem, this paper uses the multi-objective bacterial foraging optimization algorithm to select the feature subsets and k-nearest neighbor algorithm as the evaluation algorithm. The wheel roulette mechanism is further introduced to remove duplicated features. Four information exchange mechanisms are integrated into the bacteria-inspired algorithm to avoid the individuals getting trapped into the local optima so as to achieve better results in solving high-dimensional feature selection problem. On six small datasets and ten high-dimensional datasets, comparative experiments with different conventional wrapper methods and several evolutionary algorithms demonstrate the superiority of the proposed bacteria-inspired based feature selection method.

The explosive usage of social media produces massive amount of unlabeled and high-dimensional data. Feature selection has been proven to be effective in dealing with high-dimensional data for efficient learning and data mining. Unsupervised feature selection remains a challenging task due to the absence of label information based on which feature relevance is often assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, e.g., social media data is inherently linked, which makes invalid the independent and identically distributed assumption, bringing about new challenges to unsupervised feature selection algorithms. In this paper, we investigate a novel problem of feature selection for social media data in an unsupervised scenario. In particular, we analyze the differences between social media data and traditional attribute-value data, investigate how the relations extracted from linked data can be exploited to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We systematically design and conduct systemic experiments to evaluate the proposed framework on data sets from real-world social media websites. The empirical study demonstrates the effectiveness and potential of our proposed framework.

Feature selection is an important step of data processing. When feature selection is conducted for multi-label classification problem in online learning fashion, it is the problem of online multi-label feature selection. Online feature selection is very appropriate for some actual situations in which the data is not available in advance, the data size is very large or fast running speed is highly demanding. We propose an online multi-label feature selection algorithm in which the data set is divided into many single-label data sets, feature selection is conducted for each single-label data set and the final features are selected from the selected single-label features. As many data sets are imbalanced, we use the basic idea of cost-sensitive learning to combat it. Experiment results corroborate the performance of our algorithm on various data sets and demonstrate that the proposed algorithm can improve online classification performance on imbalanced data sets effectively.