Assisting Attraction Classification by Harvesting Web Data

Shen, Junge; En-rang, Zheng; Cheng, Zhiyong; Deng, Cheng

doi:10.1109/access.2017.2656878

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…The development of automated approaches employing computational methods using data from publicly available drugs datasets for the prediction of drug side effects has been proposed in [19]. [25] propose a novel framework for automatically attraction classification in leveraging web-harvesting data. Dai et al [8], Khalil et al [13] and Samulowitz and Memisevic [26] proposed to learn a greedy algorithm that behaves like a meta-algorithm which imcrementaly construct a solution for graph optimization problems.…”

Section: Algorithm Learningmentioning

confidence: 99%

A Learning-Based Approach for Agile Satellite Onboard Scheduling

Chen

2020

IEEE Access

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Autonomy increases the ability of earth observing satellites by allowing them to acquire more images. This is enabled by an efficient planning and scheduling algorithm which is able to make quick decisions onboard. Due to the NP-hardness of the agile earth observing satellite (AEOS) onboard scheduling problem, heuristic and metaheuristic algorithms seem to be appropriate to cope with increasingly enlarged problems. Also, the algorithms need to be intelligent enough to deal with dynamically changing situations onboard. Such algorithms are missing in the literature and we make the first attempt to propose a learning-based approach (LBA) for the AEOS onboard scheduling problem. LBA adopts an offline trainingonboard scheduling paradigm where it trains a classifier using massive historical data offline on the ground and embeds this classifier to an onboard greedy construction algorithm. At each construction step, the greedy algorithm uses the classifier to test the potential of a task and arranges its observation time if it is accepted by the classifier. Extensive experimental results show that the proposed LBA is highly suitable for onboard use in terms of both solution quality and response time. In particular, LBA easily dominates state-of-the-art algorithms by producing very high quality solutions for large-size problems (with over 100 tasks) in seconds. INDEX TERMS Agile autonomous satellite, onboard scheduling, learning algorithm, feature selection.

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Section: Algorithm Learningmentioning

confidence: 99%

A Learning-Based Approach for Agile Satellite Onboard Scheduling

Chen

2020

IEEE Access

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“…In real world applications, incompleteness and uncertainty in th data can be formed by various factors: sensor failure, measurement errors and unreliable features [19]- [21]. Many approaches have been proposed to handle incompleteness with supervised tasks [22]- [26]. They normally impute the incomplete features…”

Section: Introductionmentioning

confidence: 99%

K-Means Clustering With Incomplete Data

Wang

et al. 2019

IEEE Access

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Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with incomplete features which is common in practical applications. To address this issue, we propose a novel K-means based clustering algorithm which unifies the clustering and imputation into one single objective function. It makes these two processes be negotiable with each other to achieve optimality. Furthermore, we design an alternate optimization algorithm to solve the resultant optimization problem and theoretically prove its convergence. The comprehensive experimental study has been conducted on nine UCI benchmark datasets and real-world applications to evaluate the performance of the proposed algorithm, and the experimental results have clearly demonstrated the effectiveness of our algorithm which outperforms several commonly-used methods for incomplete data clustering.

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“…The advent of the Internet and online businesses to support Web Information of services (WIS) is the next level of complexity that provides the flexibility to integrate applications through standard protocols. Classification of services by different mechanisms makes it easier to recognize WIS [1], [2], [3]. They have thousands of communities to make decisions and connect the appropriate category of services, and even WIS are by far the largest interpretation without the need for a unique link that requiring the knowledge of multi-level hierarchical classifications [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Semantic based Terms Relation Research for Classification in Web Information Mining

Thota¹,

Mahalakshmi²

2019

IJRTE

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The rapid growth of web information and its services in different areas such as e-commerce, healthcare, digital marketing, online booking, etc. is a challenge in providing accurate information in the domain services related to the user's query. The current web information of services classifies the retrieval of the relevant service and assists the classification by supporting the knowledge and classifications of the specific service information. Because of these limitations and the complexity of automatic update mechanisms to see this service information, a large number of non-related service information for a requested query, and getting the required web information of services is a cumbersome problem. This paper proposes Semantic based Terms Relation Approach (STRA) for classifying information for effective classification of WIS on the web. The approach utilize Concept Terms Similarity (CTS) method for the most relevant terms in a service domain and construct a Related Terms Hierarchal Model (RTHM), which will be used for classification. A modified Naive Bayes classifier is used to perform the classification of the web information of services using RTHM, to categorize and present accurately. The experiment evaluation of the proposed approach shows an improvement in the classification of information and achieve a highly related matching results against different number of users queries.

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Assisting Attraction Classification by Harvesting Web Data

Cited by 8 publications

References 29 publications

A Learning-Based Approach for Agile Satellite Onboard Scheduling

A Learning-Based Approach for Agile Satellite Onboard Scheduling

K-Means Clustering With Incomplete Data

A Semantic based Terms Relation Research for Classification in Web Information Mining

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