A scheme for high level data classification using random walk and network measures

Cupertino, Thiago Henrique; Carneiro, Murillo G.; Zheng, Qian; Zhang, Junbao; Zhao, Liang

doi:10.1016/j.eswa.2017.09.014

Cited by 22 publications

(21 citation statements)

References 32 publications

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“…Random Walk is a mathematical statistical model [44,45], which has a wide range of applications. For example, researchers have studied the application of the Random Walk model in finance prediction [46], high level data classification [47] and social network optimization [48,49]. A one-dimensional random walk can be looked at as a Markov chain whose state space is given by the integers τ (τ = 0, ±1, ±2, .…”

Section: The Optimal State Calculatingmentioning

confidence: 99%

A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

Huang

Peng

2019

Energies

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Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city. In BSS, stations becoming empty or full is the main cause of customers failing to rent or return bikes. Some truck-based rebalancing strategies are proposed to solve this problem. However, there are still challenges around the relocation of bikes. The truck operating costs also need to be considered. In this paper, we propose a customer-oriented rebalancing strategy to solve this problem. In our strategy, two algorithms are proposed to ensure the whole system is balanced for as long as possible. The first algorithm calculates the optimal state of each station through the one-dimensional Random Walk Process with two absorption walls. Based on the derived optimal state of each station, the second algorithm recommends the station that has the largest difference between its current state and its optimal state to the customer. In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy. The simulation results indicate that the proposed strategy is able to effectively decrease the imbalance in the system and increase the system’s performance compared with the truck-based methods.

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Section: The Optimal State Calculatingmentioning

confidence: 99%

A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

Huang

Peng

2019

Energies

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“…Using graph classification has recently gained popularity and numerous works ( [2], [3], [4], [5], [6], [7], [8]) focus on using this approach instead of the classical methods of classification . These method can capture complex patterns in the data and they can generate high level features to guide the classification procedure, furthermore they can usually be modified to utilize the low level features of the data as well.…”

Section: Related Workmentioning

confidence: 99%

“…In [2] a random walker is used to classify unlabeled instances on the graph embedding of the data. This graph is represented by a weight matrix of similarities.…”

Section: Related Workmentioning

confidence: 99%

“…Classification using graph representation is studied extensively in numerous works ( [2], [3], [4], [5], [6], [7], [8], [9]). These works use graph properties such as clustering coefficient, modularity, importance, PageRank and others to classify the unlabeled data and they tend to achieve more accurate results compared to the classifiers that classify based on the low level features of data.…”

Section: Introductionmentioning

confidence: 99%

“…These works use graph properties such as clustering coefficient, modularity, importance, PageRank and others to classify the unlabeled data and they tend to achieve more accurate results compared to the classifiers that classify based on the low level features of data. This approach has been used in text classification [10], hyperspectral image classification [11], [12], image classification [2], [8], handwritten digits recognition [3] and other areas.…”

Section: Introductionmentioning

confidence: 99%

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Classification using link prediction

Fadaee

Haeri

2019

Neurocomputing

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Link prediction in a graph is the problem of detecting the missing links that would be formed in the near future. Using a graph representation of the data, we can convert the problem of classification to the problem of link prediction which aims at finding the missing links between the unlabeled data (unlabeled nodes) and their classes. To our knowledge, despite the fact that numerous algorithms use the graph representation of the data for classification, none are using link prediction as the heart of their classifying procedure. In this work, we propose a novel algorithm called CULP (Classification Using Link Prediction) which uses a new structure namely Label Embedded Graph or LEG and a link predictor to find the class of the unlabeled data. Different link predictors along with Compatibility Scorea new link predictor we proposed that is designed specifically for our settings -has been used and showed promising results for classifying different datasets. This paper further improved CULP by designing an extension called CULM which uses a majority vote (hence the M in the acronym) procedure with weights proportional to the predictions' confidences to use the predictive power of multiple link predictors and also exploits the low level features of the data. Extensive experimental evaluations shows that both CULP and CULM are highly accurate and competitive with the cutting edge graph classifiers and general classifiers.

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