3,172 research outputs found

    Cost-Sensitive Decision Tree with Multiple Resource Constraints

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    Resource constraints are commonly found in classification tasks. For example, there could be a budget limit on implementation and a deadline for finishing the classification task. Applying the top-down approach for tree induction in this situation may have significant drawbacks. In particular, it is difficult, especially in an early stage of tree induction, to assess an attribute’s contribution to improving the total implementation cost and its impact on attribute selection in later stages because of the deadline constraint. To address this problem, we propose an innovative algorithm, namely, the Cost-Sensitive Associative Tree (CAT) algorithm. Essentially, the algorithm first extracts and retains association classification rules from the training data which satisfy resource constraints, and then uses the rules to construct the final decision tree. The approach has advantages over the traditional top-down approach, first because only feasible classification rules are considered in the tree induction and, second, because their costs and resource use are known. In contrast, in the top-down approach, the information is not available for selecting splitting attributes. The experiment results show that the CAT algorithm significantly outperforms the top-down approach and adapts very well to available resources.Cost-sensitive learning, mining methods and algorithms, decision trees

    Learner Characteristics and Learners’ Inclination towards Particular Learning Environments

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    In addition to a face-to-face classroom learning environment, today’s learners in higher education are likely to have also experienced a blended learning or an online learning environment. These learning environments not only differ in their delivery modes, but also learning activities, class interactions, assessment approaches, etc. Learners tend to have differing perceptions about the effectiveness of different learning environments. This study therefore investigates whether the reasons learners like or dislike a learning environment reveal learner characteristics that may explain why some learners are more inclined towards a particular learning environment. This study also examines whether learner demographics influence learner characteristics and their preference for a particular learning environment. Using an exploratory sequential mixed methods research design, this study first conducted several focus group discussions and then administered an online questionnaire survey to collect input from students at a local university. Analyses derived four learner characteristics (i.e. desire for direct support, digital readiness, learning independence, and online hesitancy) based on the reasons why the students liked or disliked face-to-face classroom learning, blended learning, or online learning environments. A cluster analysis further distinguished the students into three groups (i.e. classroom learners, insecure learners, and online learners) based on the four learner characteristics. Analyses also found that learners’ demographics largely had no effect on learners’ characteristics and their preference for a particular learning environment. The findings suggest that learner characteristics may provide a clue to why certain learners have a preference for a face-to-face classroom learning, a blended learning, or an online learning environment. A better understanding of the relationship between learner characteristics and learners’ inclination towards a particular learning environment can be helpful to educational institutions and academics to design a range of engaging learning activities for learners with different characteristics

    Warranting value of information and tourists' trust in online booking websites

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    When tourists are planning to travel to places where they need to stay overnight, many would most likely visit some online booking websites to search for the right accommodation. Such websites as Agoda, Booking.com, Expedia, Hotels.com, etc. list accommodation vacancies and provide relevant information such as price, hotel star ratings, customer review ratings, reviewers' comments, hotel policies and facilities, and so on. This information is particularly useful for first-time tourists to a new place, helping them to reduce uncertainty in making choices. Thus, the question of which information adds more value to the decision-making process of these tourists is worth investigating. With reference to Walther and Parks's (2002) Warranting Theory and McKnight et al.'s (2002) Web Trust Model, this study develops a model to examine which information has high or low warranting value; how warranting value affects trusting intentions; and how trusting intentions affect trust-related behaviors (Willingness to depend and Subjective probability of depending as subconstructs). This study collected responses from people who have used online booking websites and analyzed the data using the partial least squares (PLS) approach. The findings of this study can help online booking websites to first understand what information is considered valuable by their customers when making accommodation choices, and subsequently, to improve website functions and features to provide information of high warranting value

    Warranting value of information and tourists' trust in online booking websites

    Get PDF
    When tourists are planning to travel to places where they need to stay overnight, many would most likely visit some online booking websites to search for the right accommodation. Such websites as Agoda, Booking.com, Expedia, Hotels.com, etc. list accommodation vacancies and provide relevant information such as price, hotel star ratings, customer review ratings, reviewers' comments, hotel policies and facilities, and so on. This information is particularly useful for first-time tourists to a new place, helping them to reduce uncertainty in making choices. Thus, the question of which information adds more value to the decision-making process of these tourists is worth investigating. With reference to Walther and Parks's (2002) Warranting Theory and McKnight et al.'s (2002) Web Trust Model, this study develops a model to examine which information has high or low warranting value; how warranting value affects trusting intentions; and how trusting intentions affect trust-related behaviors (Willingness to depend and Subjective probability of depending as subconstructs). This study collected responses from people who have used online booking websites and analyzed the data using the partial least squares (PLS) approach. The findings of this study can help online booking websites to first understand what information is considered valuable by their customers when making accommodation choices, and subsequently, to improve website functions and features to provide information of high warranting value
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