A network flow model for clustering segments and minimizing total maintenance and rehabilitation cost

Because of shrinking budgets, transportation agencies are facing severe challenges in the preservation of deteriorating pavements. There is an urgent need to develop a methodology that minimizes maintenance and rehabilitation (M&R) cost. To minimize total network M&R cost of clustering pavement segments, we propose an integer programming model similar to an uncapacitated facility location problem (UFLP) that clusters pavement segments contiguously. Based on the properties of contiguous clustered pavement segments, we have transformed the clustering problem into an equivalent network flow problem in which each possible clustering corresponds to a path in the proposed acyclic network model. Our proposed shortest-path algorithm gives an optimal clustering of segments that can be calculated in a time polynomial to the number of segments. Computational experiments indicate our proposed network model and algorithm can efficiently deal with real-world spatial clustering problems.     

基于抽样的影响力传播成本最小化算法

定义具有概率影响扩散保证的最小代价种子选择问题,验证了该问题是NP难的,且其影响函数是单调且次模的.将LT模型下的传播网络看成一个不确定图,对不确定图的可能世界进行抽样.为降低计算复杂度,提出一种对抽样图进行路径计数的算法来估计影响传播,使用VC维估计抽样图的数量.基于贪婪方法,提出一种求解该问题的算法,对该算法的误差...     

Method for minimizing the total energy of quantum-mechanical systems

A procedure is proposed for minimizing the functional for a system of singular spectral problems with dependent parameters, which can be applied in quantum mechanics.Translated from Kibernetika, No. 3, pp. 46–52, May–June, 1991.     

Adaptive slices for acquisition of anisotropic BRDF

Computational Visual Media - BRDF continues to be used as a fundamental tool for representing material appearance in computer graphics. In this paper we present a practical adaptive method for...     

Using GA to dispatch the obtaining quantity for minimizing the total cost based on consideration of patient safety in HSCM

The objective of this paper is to study how to minimize the total system cost (TSC) when patient safety is considered in the hospital’s supply chain management (HSCM). Owing to the HSCM explored in material flow, service level (S.L) is used as the index of patient safety level in this paper. HSCM includes three hospitals and a centralized purchasing center (CPC). A genetic algorithm (GA) is applied in the CPC to compute the dispatched quantity for each hospital. The results show that the CPC coordinates the total obtaining quantity for each hospital and assists the hospital’s decision makers to estimate the total cost for each hospital. Also, the sensitivity analysis shows that to increase the same S.L for each hospital, these three hospitals should have almost the same incremental total cost when one unit quantity is dispatched.     

Dimensionality reduction by minimizing nearest-neighbor classification error

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques.     

A maximum-margin genetic algorithm for misclassification cost minimizing feature selection problem

We consider a feature selection problem where the decision-making objective is to minimize overall misclassification cost by selecting relevant features from a training dataset. We propose a two-stage solution approach for solving misclassification cost minimizing feature selection (MCMFS) problem. Additionally, we propose a maximum-margin genetic algorithm (MMGA) that maximizes margin of separation between classes by taking into account all examples as opposed to maximizing margin of separation using a few support vectors. Feature selection is carried out by either an exhaustive or a heuristic simulated annealing approach in the first stage and a cost sensitive classification using either MMGA or cost sensitive support vector machines (SVM) in the second stage. Using simulated and real-world data sets and different misclassification cost matrices, we test our two-stage approach for solving the MCMFS problem. Our results indicate that feature selection plays an important role when misclassification cost asymmetries increase and the MMGA shows equal or better performance than the SVM.     

Building cost functions minimizing to some summary statistics

A learning machine-or a model-is usually trained by minimizing a given criterion (the expectation of the cost function), measuring the discrepancy between the model output and the desired output. As is already well known, the choice of the cost function has a profound impact on the probabilistic interpretation of the output of the model, after training. In this work, we use the calculus of variations in order to tackle this problem. In particular, we derive necessary and sufficient conditions on the cost function ensuring that the output of the trained model approximates 1) the conditional expectation of the desired output given the explanatory variables; 2) the conditional median (and, more generally the q-quantile); 3) the conditional geometric mean; and 4) the conditional variance. The same method could be applied to the estimation of other summary statistics as well. We also argue that the least absolute deviations criterion could, in some cases, act as an alternative to the ordinary least squares criterion for nonlinear regression. In the same vein, the concept of "regression quantile" is briefly discussed.     

Adaptive crawler for external hyperlinks search and acquisition

We describe a search robot (crawler) intended to collect information regarding outgoing hyperlinks from a given set of web sites related to a certain topic. The crawler’s adaptive behavior is formulated in terms of a multi-armed bandit problem. Our experiments show that the choice of an adaptive algorithm for the crawler’s rational behavior depends on the actual topic of the underlying set of web sites.     

Elite guided steady-state genetic algorithm for minimizing total tardiness in flowshops

In this research, a detailed study of the permutation flowshop scheduling problem with the objective of minimizing total tardiness is presented and a steady-state genetic algorithm solution procedure is developed for such problems. Also, using problem-specific knowledge, a very efficient elite guided solution improvement scheme and an appropriate crossover operator have been developed and integrated into the proposed method. Using benchmark problems, the algorithm has been compared with heuristic algorithms having the best performance in the literature. The performance of the developed algorithm is shown to be superior using a simulation study.     

Adaptive machine learning classification for diabetic retinopathy

Multimedia Tools and Applications - Diabetic retinopathy is the main cause of the blindness worldwide. However, the diabetic retinopathy is hard to be detected in the initial stages, and the...     

Cost-sensitive feature acquisition and classification

There are many sensing challenges for which one must balance the effectiveness of a given measurement with the associated sensing cost. For example, when performing a diagnosis a doctor must balance the cost and benefit of a given test (measurement), and the decision to stop sensing (stop performing tests) must account for the risk to the patient and doctor (malpractice) for a given diagnosis based on observed data. This motivates a cost-sensitive classification problem in which the features (sensing results) are not given a priori; the algorithm determines which features to acquire next, as well as when to stop sensing and make a classification decision based on previous observations (accounting for the costs of various types of errors, as well as the rewards of being correct). We formally define the cost-sensitive classification problem and solve it via a partially observable Markov decision process (POMDP). While the POMDP constitutes an intuitively appealing formulation, the intrinsic properties of classification tasks resist application of it to this problem. We circumvent the difficulties of the POMDP via a myopic approach, with an adaptive stopping criterion linked to the standard POMDP. The myopic algorithm is computationally feasible, easily handles continuous features, and seamlessly avoids repeated actions. Experiments with several benchmark data sets show that the proposed method yields state-of-the-art performance, and importantly our method uses only a small fraction of the features that are generally used in competitive approaches.     

A queuing network model for minimizing the total makespan of computational grids

This paper offers a mathematical solution based on queuing theory and a generalized stochastic Petri net model to minimize the total makespan of the grid computing environments. A grid manager could minimize the total makespan through cautious distribution of subtasks to the grid resources. Subtask arrival rates depend on the arrival rate of the grid tasks submitted to the grid manager, local tasks directly submitted to the grid resources and the processing speed of the resources. Modeling the grid environment using queuing network, the steady state analysis of the network will result in the mean response time of the resources. Therefore, the total makespan could be minimized by minimizing the longest mean response time of the resources. The accuracy of the values obtained for the subtasks arrival rates at each of the grid resources from solving the corresponding queuing network could be further evaluated by steady state analysis of the generalized stochastic Petri net modeling the same grid environment.     

Adaptive linear discriminant regression classification for face recognition

Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each training sample and ignores the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, in this paper, we propose an adaptive linear discriminant regression classification (ALDRC) algorithm by taking special consideration of different contributions of the training samples. Specifically, ALDRC makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and then ALDRC seeks to find an optimal projection matrix that can maximize the ratio of the between-class reconstruction error over the within-class reconstruction error. Extensive experiments carried out on the AR, FERET and ORL face databases demonstrate the effectiveness of the proposed method.     

Lower bounds for minimizing total completion time in a two-machine flow shop

For the -hard problem of scheduling n jobs in a two-machine flow shop so as to minimize the total completion time, we present two equivalent lower bounds that are computable in polynomial time. We formulate the problem by the use of positional completion time variables, which results in two integer linear programming formulations with O(n ²) variables and O(n) constraints. Solving the linear programming relaxation renders a very strong lower bound with an average relative gap of only 0.8% for instances with more than 30 jobs. We further show that relaxing the formulation in terms of positional completion times by applying Lagrangean relaxation yields the same bound, no matter which set of constraints we relax.     

Adaptive binary tree for fast SVM multiclass classification

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.     

Adaptive basis functions for prototype-based classification of functional data

We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data and time series by means of generalized matrix relevance learning vector quantization (GMLVQ) as an example. To take advantage of the functional nature, a functional expansion of the input data is considered. Instead of using a predefined set of basis functions for the expansion, a more flexible scheme of an adaptive functional basis is employed. GMLVQ is applied on the resulting functional parameters to solve the classification task. For comparison of the classification, a GMLVQ system is also applied to the raw input data, as well as on data expanded by a different predefined functional basis. Computer experiments show that the methods offer potential to improve classification performance significantly. Furthermore, the analysis of the adapted set of basis functions give further insights into the data structure and yields an option for a drastic reduction of dimensionality.

     

Note on minimizing total tardiness in a two-machine flowshop

This note considers the problem of sequencing jobs to minimize total tardiness in a two-machine flowshop. The note shows how three dominance conditions and a lower bound previously developed for this problem can be improved. The note also proposes a new dominance condition. A branch-and-bound algorithm is developed that uses the improvements and new dominance condition. The algorithm is tested on randomly generated problems and the results of the test show that the improvements and new dominance condition improves the branch-and-bound algorithm's efficiency.