An embedded feature selection method for imbalanced data classification

Liu, Haoyue; Zhou, MengChu; Liu, Qing

doi:10.1109/jas.2019.1911447

Cited by 313 publications

(105 citation statements)

References 34 publications

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“…However, the performance of F-measure achieved excellent performance only if 20% or more of features were chosen. The results were helpful for practitioners to select a proper feature selection method when facing a practical problem [18].…”

Section: Table Imentioning

confidence: 97%

A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

2020

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Due to the remarkable increase in e-commerce transactions, people try to have an appropriate choice of purchase through considering other people's reflected experience in product's or service's reviews. Automatic analysis of such corpus requires enhanced developed algorithms based on natural language processing and opinion mining. Moreover, the linguistic differences make extending existing algorithms from one language to another challenging and in some cases impossible. Opinion mining focuses on different subjects of review analysis such as spam detection, aspect elicitation and polarity allocation. In this paper, we focus on detection of explicit aspect and propose a methodology to overcome some difficult and problematic aspect compounds in the form of multi-words format in Persian language. Our approach proposes the construction of a directed weighted graph (ADG structure) based on some yielded information from FP-Growth frequent pattern identification algorithm on our corpus of Persian sentence. Traversing some special paths within the ADG graph according to our developed rules could lead us to the extraction of problematic multi-word aspects. We utilize Neo4j NoSQL graph database environment and its Cypher query language in order to create the ADG graph and access the desired paths that reflects our developed rules on the ADG structure which lead us to extract the multi-word aspects. The evaluation of our methodology with the existing approaches on the issue of aspect derivation in Persian language including ELDA, SAM, an MMI-based and an LRT-based algorithms indicates the robustness of our approach.

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Section: Table Imentioning

confidence: 97%

A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

2020

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“…Compared with the state-of-theart algorithms, the proposed algorithm could acquire better classification performance on the 27 imbalanced data sets. Liu et al [25] developed an embedded feature selection algorithm named weighted Gini index by adding an index-weighting method to classification and regression tree to deal with the class imbalanced problem. Experiments shown that WGI could achieve the better performance only if 20% or more of features are chosen compared to Chi2, F-statistic and Gini index.…”

Section: Related Work a Cost-sensitive Learning Algorithmmentioning

confidence: 99%

Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Feng

Shen

et al. 2020

IEEE Access

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Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and stateof-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results. INDEX TERMS Imbalanced data, cost-sensitive, general vector machine, binary ant lion optimizer.

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“…A. DATA PREPROCESSING An effective feature selection method is critical to the preprocessing of a dataset [51]. A dataset of historical resource demands includes a large number of containers of various types.…”

Section: An Adaptive Prediction Algorithm For Cloud Resource Demandsmentioning

confidence: 99%

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Chen

Wang

2020

IEEE Access

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Cloud computing has been widely applied in various fields with the development of big data and artificial intelligence. The associated resource demands exhibit characteristics such as diversity, large scale, burst and uncertainty. This paper analyzes these characteristics of cloud resource demands based on Alibaba cluster data, and proposes an adaptive short-term prediction algorithm for those demands. The proposed algorithm uses a principal component analysis method to extract the primary types of container demands from a time series of resource demands, and executes outlier detection and replacement to obtain a more stationary sequence. An adaptive short-term prediction strategy is proposed to adaptively select a higher-accuracy short-term prediction method to implement the prediction. Further, an error adjustment factor is proposed to reduce the prediction error. Thus, the short-term prediction accuracy of cloud resource demands is improved via outlier detection and replacement, an adaptive selection strategy and an error adjustment. We evaluated the effectiveness of these improvements, and compared our algorithm with existing algorithms in terms of effectiveness and time cost. The experimental results demonstrate that the proposed algorithm improves short-term prediction accuracy effectively.

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An embedded feature selection method for imbalanced data classification

Cited by 313 publications

References 34 publications

A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

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