Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation

Feature selection (FS) has become an indispensable task in dealing with today's highly complex pattern recognition problems with massive number of features. In this study, we propose a new wrapper approach for FS based on binary simultaneous perturbation stochastic approximation (BSPSA). This pseudo-gradient descent stochastic algorithm starts with an initial feature vector and moves toward the optimal feature vector via successive iterations. In each iteration, the current feature vector's individual components are perturbed simultaneously by random offsets from a qualified probability distribution. We present computational experiments on datasets with numbers of features ranging from a few dozens to thousands using three widely-used classifiers as wrappers: nearest neighbor, decision tree, and linear support vector machine. We compare our methodology against the full set of features as well as a binary genetic algorithm and sequential FS methods using cross-validated classification error rate and AUC as the performance criteria. Our results indicate that features selected by BSPSA compare favorably to alternative methods in general and BSPSA can yield superior feature sets for datasets with tens of thousands of features by examining an extremely small fraction of the solution space. We are not aware of any other wrapper FS methods that are computationally feasible with good convergence properties for such large datasets.Comment: This is the Istanbul Sehir University Technical Report #SHR-ISE-2016.01. A short version of this report has been accepted for publication at Pattern Recognition Letter

Optimal obstacle placement with disambiguations

We introduce the optimal obstacle placement with disambiguations problem wherein the goal is to place true obstacles in an environment cluttered with false obstacles so as to maximize the total traversal length of a navigating agent (NAVA). Prior to the traversal, the NAVA is given location information and probabilistic estimates of each disk-shaped hindrance (hereinafter referred to as disk) being a true obstacle. The NAVA can disambiguate a disk's status only when situated on its boundary. There exists an obstacle placing agent (OPA) that locates obstacles prior to the NAVA's traversal. The goal of the OPA is to place true obstacles in between the clutter in such a way that the NAVA's traversal length is maximized in a game-theoretic sense. We assume the OPA knows the clutter spatial distribution type, but not the exact locations of clutter disks. We analyze the traversal length using repeated measures analysis of variance for various obstacle number, obstacle placing scheme and clutter spatial distribution type combinations in order to identify the optimal combination. Our results indicate that as the clutter becomes more regular (clustered), the NAVA's traversal length gets longer (shorter). On the other hand, the traversal length tends to follow a concave-down trend as the number of obstacles increases. We also provide a case study on a real-world maritime minefield data set.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS556 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A stochastic approximation approach to spatio-temporal anchorage planning with multiple objectives

Globalization and subsequent increase in seaborne trade have necessitated efficient planning and management of world's anchorage areas. These areas serve as a temporary stay area for commercial vessels for various reasons such as waiting for passage or port, fuel services, and bad weather conditions. The research question we consider in this study is how to place these vessels inside a polygon-shaped anchorage area in a dynamic fashion as they arrive and depart, which seems to be the first of its kind in the literature. We specifically take into account the objectives of (1) anchorage area utilization, (2) risk of vessel collisions, and (3) fuel consumption performance. These three objectives define our objective function in a weighted sum scheme. We present a spatio-temporal methodology for this multi-objective anchorage planning problem where we use Monte Carlo simulations to measure the effect of any particular combination of planning metrics (measured in real time for an incoming vessel) on the objective function (measured in steady state). We resort to the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm for identifying the linear combination of the planning metrics that optimizes the objective function. We present computational experiments on a major Istanbul Straight anchorage, which is one of the busiest in the world, as well as synthetic anchorages. Our results indicate that our methodology significantly outperforms comparable algorithms in the literature for daily anchorage planning. For the Istanbul Straight anchorage, for instance, reduction in risk was 42% whereas reduction in fuel costs was 45% when compared the best of the current state-of-the-art methods. Our methodology can be utilized within a planning expert system that intelligently places incoming vessels inside the anchorage so as to optimize multiple strategic goals. Given the flexibility of our approach in terms of the planning objectives, it can easily be adapted to more general variants of multi-objective spatio-temporal planning problems where certain objects need to be dynamically placed inside two or even-three dimensional spaces in an intelligent manner.Accepted Author ManuscriptTransport and Plannin

Optimal ship navigation with safety distance and realistic turn constraints

Abstract We consider the optimal ship navigation problem wherein the goal is to find the shortest path between two given coordinates in the presence of obstacles subject to safety distance and turn-radius constraints. These obstacles can be debris, rock formations, small islands, ice blocks, other ships, or even an entire coastline. We present a graph-theoretic solution on an appropriately-weighted directed graph representation of the navigation area obtained via 8-adjacency integer lattice discretization and utilization of the A∗ algorithm. We explicitly account for the following three conditions as part of the turn-radius constraints: (1) the ship’s left and right turn radii are different, (2) ship’s speed reduces while turning, and (3) the ship needs to navigate a certain minimum number of lattice edges along a straight line before making any turns. The last constraint ensures that the navigation area can be discretized at any desired resolution. Once the optimal (discrete) path is determined, we smoothen it to emulate the actual navigation of the ship. We illustrate our methodology on an ice navigation example involving a 100,000 DWT merchant ship and present a proof-of-concept by simulating the ship’s path in a full-mission ship handling simulator

When does daylight saving time save electricity? Weather and air-conditioning

Previous research on the effects of daylight saving time (DST) on electricity consumption has provided mixed results. We use daily state-level panel data on electricity consumption in Australia between 1998 and 2015, during which period there was considerable variation in the presence and timing of DST implementation, as well as in weather conditions and cooling usage within and between states. This provides us with a unique opportunity to study the interaction effects of DST with exogenous variation in daily weather conditions and cooling usage over two decades. Our results show that the effect of DST on electricity consumption depends strongly on weather conditions and cooling usage. Forward DST increases the electricity consumption when temperatures and air conditioner ownership are higher. We provide simulations for countries in the European Union that need to decide on DST adoption in the coming year. Our findings are policy-relevant given rising temperatures and worldwide increases in cooling usage during summer. © 2021 Elsevier B.V