178,876 research outputs found
Uplift Modeling with Multiple Treatments and General Response Types
Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods
Weblogs in Higher Education - Why Do Students (Not) Blog?
Positive impacts on learning through blogging, such as active knowledge construction and reflective writing, have been reported. However, not many students use weblogs in informal contexts, even when appropriate facilities are offered by their universities. While motivations for blogging have been subject to empirical studies, little research has addressed the issue of why students choose not to blog. This paper presents an empirical study undertaken to gain insights into the decision making process of students when deciding whether to keep a blog or not. A better understanding of students' motivations for (not) blogging may help decision makers at universities in the process of selecting, introducing, and maintaining similar services. As informal learning gains increased recognition, results of this study can help to advance appropriate designs of informal learning contexts in Higher Education. The method of ethnographic decision tree modelling was applied in an empirical study conducted at the Vienna University of Technology, Austria. Since 2004, the university has been offering free weblog accounts for all students and staff members upon entering school, not bound to any course or exam. Qualitative, open interviews were held with 3 active bloggers, 3 former bloggers, and 3 non‑ bloggers to elicit their decision criteria. Decision tree models were developed out of the interviews. It turned out that the modelling worked best when splitting the decision process into two parts: one model representing decisions on whether to start a weblog at all, and a second model representing criteria on whether to continue with a weblog once it was set up. The models were tested for their validity through questionnaires developed out of the decision tree models. 30 questionnaires have been distributed to bloggers, former bloggers and non‑ bloggers. Results show that the main reasons for students not to keep a weblog include a preference for direct (online) communication, and concerns about the loss of privacy through blogging. Furthermore, the results indicate that intrinsic motivation factors keep students blogging, whereas stopping a weblog is mostly attributable to external factors
A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks
In this paper we propose a distributed implementation of the relaxed
Alternating Direction Method of Multipliers algorithm (R-ADMM) for optimization
of a separable convex cost function, whose terms are stored by a set of
interacting agents, one for each agent. Specifically the local cost stored by
each node is in general a function of both the state of the node and the states
of its neighbors, a framework that we refer to as `partition-based'
optimization. This framework presents a great flexibility and can be adapted to
a large number of different applications. We show that the partition-based
R-ADMM algorithm we introduce is linked to the relaxed Peaceman-Rachford
Splitting (R-PRS) operator which, historically, has been introduced in the
literature to find the zeros of sum of functions. Interestingly, making use of
non expansive operator theory, the proposed algorithm is shown to be provably
robust against random packet losses that might occur in the communication
between neighboring nodes. Finally, the effectiveness of the proposed algorithm
is confirmed by a set of compelling numerical simulations run over random
geometric graphs subject to i.i.d. random packet losses.Comment: Full version of the paper to be presented at Conference on Decision
and Control (CDC) 201
Water Flow-Like Algorithm with Simulated Annealing for Travelling Salesman Problems
Water Flow-like Algorithm (WFA) has been proved its ability obtaining a fast and quality solution for solving Travelling Salesman Problem (TSP). The WFA uses the insertion move with 2-neighbourhood search to get better flow splitting and moving decision. However, the algorithms can be improved by making a good balance between its solution search exploitation and exploration. Such improvement can be achieved by hybridizing good search algorithm with WFA. This paper presents a hybrid of WFA with various three neighbourhood search in Simulated Annealing (SA) for TSP problem. The performance of the proposed method is evaluated using 18 large TSP benchmark datasets. The experimental result shows that the hybrid method has improved the solution quality compare with the basic WFA and state of art algorithm for TSP
Communicating Criterion-Related Validity Using Expectancy Charts: A New Approach
Often, personnel selection practitioners present the results of their criterion-related validity studies to their senior leaders and other stakeholders when trying to either implement a new test or validate an existing test. It is sometimes challenging to present complex, statistical results to non-statistical audiences in a way that enables intuitive decision making. Therefore, practitioners often turn to expectancy charts to depict criterion-related validity. There are two main approaches for constructing expectancy charts (i.e., use of Taylor-Russell tables or splitting a raw dataset), both of which have considerable limitations. We propose a new approach for creating expectancy charts based on the bivariate-normal distribution. The new method overcomes the limitations inherent in the other two methods and offers a statistically sound and user-friendly approach for constructing expectancy charts
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
Optimal execution is a sequential decision-making problem for cost-saving in
algorithmic trading. Studies have found that reinforcement learning (RL) can
help decide the order-splitting sizes. However, a problem remains unsolved: how
to place limit orders at appropriate limit prices? The key challenge lies in
the "continuous-discrete duality" of the action space. On the one hand, the
continuous action space using percentage changes in prices is preferred for
generalization. On the other hand, the trader eventually needs to choose limit
prices discretely due to the existence of the tick size, which requires
specialization for every single stock with different characteristics (e.g., the
liquidity and the price range). So we need continuous control for
generalization and discrete control for specialization. To this end, we propose
a hybrid RL method to combine the advantages of both of them. We first use a
continuous control agent to scope an action subset, then deploy a fine-grained
agent to choose a specific limit price. Extensive experiments show that our
method has higher sample efficiency and better training stability than existing
RL algorithms and significantly outperforms previous learning-based methods for
order execution
Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics
The issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems
An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer
The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has
resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have
been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed.
However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy
reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in
a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed.
A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem
in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility
of the model and the developed algorithm
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