SDRS: A new lossless dimensionality reduction for text corpora

Mendizabal, Iñaki Velez de; Basto‐Fernandes, Vitor; Ezpeleta, Enaitz; Méndez, J.; Zurutuza, Urko

doi:10.1016/j.ipm.2020.102249

Cited by 6 publications

(12 citation statements)

References 30 publications

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“…It is also one of the well-regarded variants of GA, and its chromosome rank is based on their level domination. Compared to the earlier variants of GA (NSGA), NSGA-II achieves a "fast nondominating sorting technique," a "crowding distance technique," and a parameterless niching operator, greatly improving the complexity and accuracy of the algorithm [29,30]. SPEA2 is an evolutionary method for multicriteria optimization proposed by Eckart Zitzler and Lothar Thiele in 1999, also known as enhanced Pareto genetic algorithm.…”

Section: Comparison With Other Well-known Multiobjectivementioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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With the rapid development of information technology, the sharing economy has developed rapidly all over the world as a new mode of distributing business profit, among which the bike-sharing system (BSS) has become popular in many cities because of its low cost, convenience, and environmental protection. The application of the 5th generation mobile communication technology (5G) in BSS makes users to search the bikes more accurately and quickly and enables operators to spot noncompliant bike sharing as soon as possible, significantly improving the efficiency of bike-sharing management. However, one of the thorny issues for operators is the bike-sharing rebalancing problem (BRP). It is the key to improve the efficiency of rebalancing, reduce the rebalancing cost, and realize the sustainable development of BSS on how to excavate the huge amount of customer cycling data, respond quickly to customer demand, and use intelligence optimization algorithm to rebalance bikes among stations. However, most of the previous studies dealt with only one period BRP and rarely considered multiperiod issues. At the same time, most researches have focused on minimizing the total cost or time of rebalancing or customer dissatisfaction, but few have aimed at minimizing the rebalancing amount. In addition, the demand gap can reflect the real rental and returning requirements of customers over a certain period of time, which is rarely considered in solving BRP. First of all, this paper presents a multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP). Secondly, a mathematical model is formulated with the objective of minimizing both the total rebalancing cost and amount. In order to solve MMBRP, an improved multiobjective backtracking search genetic algorithm (IMBSGA) is designed. Finally, the effectiveness and competitiveness of IMBSGA in solving MMBRP are verified by numerous experiments comparing with state-of-the-art algorithms.

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Section: Comparison With Other Well-known Multiobjectivementioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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“…This method reduces the complexity of the dimensions in the feature vector for the efficient retrieval of information. Mendizabal et al [10] tackles the issue of feature reduction by proposing a new semantic-based proposal which prevents a lack of (lossless) information. Synset characteristics can be classified semantically by using the BabelNet ontological dictionary's taxonomic relationships (mainly hypernyms).…”

Section: Dimension Reductionmentioning

confidence: 99%

Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

Kumar¹,

Kannammal²,

Krishnamoorthy³

et al. 2020

ITC

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Document clustering plays a significant task in the retrieval of the information, which seeks to divide documents into groups automatically, depending on their content similarity. The cluster consists of related documents within the group (having high intra-cluster similarity) and dissimilar to other group documents (having low inter-cluster similarity). Clustering documents should be considered an unsupervised process that aims to classify documents by identifying underlying structures, i.e. the learning process is unsupervised. So there is no need to determine the correct output for an input. Previous clustering methods do not know the semantic associations between words such that the context of documents cannot be correctly interpreted. In order to address this problem, the advent of semantic ontology information such as WordNet was widely used to enhance text clustering consistency. This paper initially proposes an OntoVSM model to reduce the dimension of the document efficiently. The cover K-means clustering algorithm is proposed for semantic document clustering. The proposed algorithm is a hybrid version of K-Means and covers coefficient-based clustering methodology (C3M) that is improved semantically using WordNet ontology. The dimensionality reduction based on semantic knowledge of each term preserves the information without loss. The performance of the proposed work is analysed through experimental results. This shows that the proposed work gives improved results compared to other standard methods.

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“…Word segmentation plays an important role in the search field, data mining and text classification. The quality of word segmentation directly affects the accuracy of the following models [15]. Word segmentation, also known as word segmentation, is a process of combining the necessary types of continuous self sequenced vouchers from scratch into word sequences, and is also a key step in transforming text from unstructured data to structured data.…”

Section: Message Content Pre-processingmentioning

confidence: 99%

Filtering Spam Messages Based on Improved Naive Bayesian Algorithm

2020

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The relative lack of system and supervision has caused many negative impacts on the "black industry" around wireless communication, such as the spam messages of mobile phones, which have always troubled people's lives. This paper focuses on the research of spam short message filtering based on the improved naive Bayes algorithm. This paper introduces the background of spam short message, and summarizes the status quo of spam short message filtering technology and filtering system construction at home and abroad. The intelligent filtering technology is optimized, and a new algorithm combining Bayesian network classification algorithm with artificial intelligence algorithm is used to filter junk short messages. The final experimental results show that the improved naive Bayesian short message filtering method proposed in this paper can improve the accuracy, recall and overall efficiency of short message filtering, while the filtering efficiency also increases with the expansion of the cluster size.

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SDRS: A new lossless dimensionality reduction for text corpora

Cited by 6 publications

References 30 publications

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

Filtering Spam Messages Based on Improved Naive Bayesian Algorithm

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