Index System Reduction Method Based on the Index Similarity

Abstract

Multi-attribute decision making (MADM) always suffers from the result inconsistency and computational complexity problem, due to numbers of redundant and relational attributes (indexes) of the initial evaluation index system. Therefore, this paper studies the index system (IS) reduction problem through selecting the most representative indicator from each index subsystem after the IS structure partition. First, we propose and demonstrate the Index Subsystem Judgement theorem to improve the efficiency of the classic system structure partition algorithm. Second, an algorithm of index system reduction based on the index similarity (ISRS) is put forward. The ISRS is able to reduce the index quantity while still keeping the index meaning. Third, we define the direction loss rate to measure the evaluation ability loss of the IS during reduction. The algorithm is tested for a synthetic dataset to compare the proposed ISRS with different index reduction algorithms, followed by an extensive experimentation with a real-world financial dataset. Experiment results illustrate that our proposed method is able to obtain more accessible and available reduction results in practice.

A New Method for Remote Sensing Satellite Observation Effectiveness Evaluation

“…The effectiveness indices are decomposed based on temporal resolution, spatial res olution, spectral resolution, and radiometric resolution. The index decomposition resul is presented in the first three columns in Figure 4 [10,[20][21][22][23]26]. The temporal resolution contains various indices, including revisit time, observation time interval, single observa tion ability, total coverage time, task response-ability, and continuous observation ability The spatial resolution includes ground sampling distance (GSD), total observation num ber, coverage percentage, and capability of discovery, identification, and tracking.…”

“…The study of effectiveness evaluation is mainly divided into two parts: the construction of the index system and the research of evaluation methodologies [9]. The index system is a whole composed of several related indices, which can completely reflect the characteristics of the evaluation object from all aspects [10]. As the main content of the evaluation study, the reasonability of the index system will have a direct impact on the effectiveness of the evaluation [11].…”

“…The index systems can well reflect all aspects of the system capabilities but inevitably include redundant indices. Zheng et al [10] established an evaluation system including revisit capability, observation interval, observation frequency, observation duration, and other indices from the perspective of three target types: point target, area target, and moving target. Barsi et al [20] established a remote sensing data quality index system including resolution, accuracy, completeness, redundancy, readability, and accessibility.…”

“…Huang et al [27] combined the AHP and EWM methods to screen out components that are closely associated with the efficacy of drugs. Based on the information entropy theory, Wang et al [10] defined the direction loss rate to measure the evaluation ability loss of the index screening results. Normally, the EWM method can evaluate and ensure the completeness of the index system.…”

A Grey Model-Least Squares Support Vector Machine Method for Time Series Prediction

“…Increasing scenarios of artificial intelligence technologies are keeping studies on time series prediction at center stage [8,34]. Especially in the economic context, large amounts of economic time series data are produced every second, and various business scenarios like the economic growth prediction, economic policy making, and social development condition analysis, motivate the continuous improvement of prediction methods [35].…”