Monotonic Tasks Research Articles

Active Antinoise Fuzzy Dominance Rough Feature Selection Using Adaptive K-Nearest Neighbors

Feature selection methods with anti-noise performance are effective dimensionality reduction methods for classification tasks with noise. However, there are few studies on robust feature selection methods for monotonic classification tasks. The fuzzy dominance rough set (FDRS) model is a nontrivial knowledge acquisition tool, which is widely used in feature selection of monotonic classification tasks. Nonetheless, this model has been proved in practice to be generally poorly fault-tolerance, and only one noisy sample can cause huge interference in acquiring knowledge. In view of these two issues, this paper firstly designs an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest neighbors strategy to calculate the density of samples. The noisy samples are identified according to their densities, and then an active anti-noise fuzzy dominance rough set model is proposed. Then, in the active anti-noise fuzzy dominance rough approximation space, the class-separability is evaluated by the approximation operators of the proposed model, and the feature-redundancy is evaluated by the fuzzy ranking conditional mutual information. On this basis, a feature evaluation index is designed comprehensively considering class-separability and feature-redundancy. Finally, a feature selection algorithm is designed to select the feature subset with the highest classification performance. The experimental results show that the proposed algorithm has better robustness and classification performance.

Approximation algorithms for scheduling monotonic moldable tasks on multiple platforms

Approximation algorithms for scheduling monotonic moldable tasks on multiple platforms

Feature Selection Considering Multiple Correlations Based on Soft Fuzzy Dominance Rough Sets for Monotonic Classification

Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified sample will lead to large errors in acquiring knowledge. This unstable phenomenon does not meet the requirements of practical applications. On the other hand, feature selection is supposedly an effective dimensionality reduction approach for classification tasks. In the real world, feature combinations with multiple correlations can often provide important classification information, where the multiple correlations include redundancy, complementarity, and interaction between features. To the best of our knowledge, most of the existing feature selection methods for MCTs only consider the relevance between features and decision, while ignoring the multiple correlations. To overcome these two drawbacks, in this article, we propose a robust fuzzy dominance rough set model, and develop a feature selection method that considers multiple correlations based on the robust model for MCTs. First, a soft fuzzy dominance rough set (SFDRS) with robustness is proposed. Second, a feature evaluation index considering multiple correlations is presented. Finally, a feature selection algorithm based on SFDRS is designed to select an optimal feature subset. Extensive experiments are conducted on 12 public datasets, and the results show that the SFDRS model has good robustness and the proposed feature selection algorithm has excellent classification performance.

An improved approximation algorithm for scheduling monotonic moldable tasks

We are concerned with the problem of scheduling monotonic moldable tasks on identical processors to minimize the makespan. We focus on the natural case where the number m of processors as resources is fixed or relatively small compared with the number n of tasks. We present an efficient (3/2)-approximation algorithm with time complexity O(nmlog(nm)) (for m>n) and O(n2logn) (for m≤n). To the best of our knowledge, the best relevant known results are: (a) a (3/2+ϵ)-approximation algorithm with time complexity O(nmlog(n/ϵ)), (b) a fully polynomial-time approximation scheme for the case of m≥16n/ϵ, and (c) a polynomial-time approximation scheme with time complexity O(ng(1/ϵ)) when m is bounded by a polynomial in n, where g(·) is a super-exponential function. On the other hand, the novel general technique developed in this paper for removing the ϵ-term in the worst-case performance ratio can be applied to improving the performance guarantee of certain dual algorithms for other combinatorial optimization problems.

The future shop-floor operators, demands, requirements and interpretations

The evolution of the manufacturing industry reveals continuous progress and development throughout the years. This evolution not only includes production methodologies and the production equipment, it also includes the working environment of the shop-floor operators. The demands faced by the shop-floor operators have developed from strictly controlled, simple and monotonic tasks to self-controlled team work requiring a holistic approach that aims at continuous improvements and achieving a high degree of flexibility, adaptability and initiative.This paper describes the evolution of the shop-floor operator, according to the research literature and interviews with manufacturing managers and human resources specialists. In addition, the paper presents the response of future Swedish shop-floor operators, today’s high-school students, to a description of their possible future work as shop-floor operators. The Swedish manufacturing industry competes, to a large extent, on and responds to the international market. The findings made in this paper are thus also interesting for other industries and countries acting on the international market.

Partially monotonic decision trees

In multicriteria decision tasks, certain features are linearly ordered according to the decision and are called criteria, whereas others, called regular attributes, are not. In practice, regular attributes and criteria coexist in most classification tasks. In this paper, we propose a rank-inconsistent rate that distinguishes attributes from criteria. Furthermore, it represents the directions of the monotonic relationships between criteria and decisions. We design a partially monotonic decision tree algorithm to extract decision rules for partially monotonic classification tasks. Experimental results show that the proposed algorithm is effective and efficient.

Why I want to be a Future Swedish Shop-floor Operator

When looking in rear view mirrors the Swedish as well as the international production industries can overview several years of progress covering all aspects of production. Production methodologies and machines etc. have changed and evolved, and so has the environment of the shop-floor operator. The demands on the shop-floor operators have grown from simple monotonic tasks with low complexity to pro-active team work requiring flexibility, continuous improvements and a holistic approach.With a base in a study where production and HR-managers at six Swedish manufacturing industries have been interviewed this paper identifies the role of today's and the future Swedish shop-floor operator. The response to the described role of the future operator is compiled from the ones who will become the future Swedish shop-floor operators – today's teenagers attending technical high-school. Their views of the environment of the future shop-floor operator are described by accuracy, development, a good working environment and team work. The paper also reveals what the offer should include to make these teenagers say: I want to be a future Swedish shop-floor operator.

Minimum energy consumption for rate monotonic scheduled tasks

Limited battery power is a typical constraint in stand-alone embedded systems. One way to extend the battery lifetime is by reducing CPU power consumption. Because of the quadratic relationship between power consumption in CMOS circuits and CPU voltage, power reduction can be obtained by scaling down supply voltage, or dynamic voltage scaling. However, reducing supply voltage slows down CPU speed since supply voltage has a proportional relationship with CPU frequency. On the other hand, in any real-time embedded environment (especially hard real-time), timing constraints are critical. In this paper, we focus on dynamic energy reduction of tasks scheduled by rate monotonic (RM) algorithm in a hard real-time embedded environment. The RM algorithm preemptively schedules any set of periodic tasks by assigning higher priorities to frequent tasks. For any periodic task set that satisfies the CPU utilization bound, we determine the provably optimal scaling of the worst-case execution time of each task that consumes minimum dynamic energy while satisfying the utilization bound. As RM algorithm is widely used, we expect this work can lead to better energy reduction management and expectations.

The Complexity of Optimal Monotonic Planning: The Bad, The Good, and The Causal Graph

For almost two decades, monotonic, or ``delete free,'' relaxation has been one of the key auxiliary tools in the practice of domain-independent deterministic planning. In the particular contexts of both satisficing and optimal planning, it underlies most state-of-the-art heuristic functions. While satisficing planning for monotonic tasks is polynomial-time, optimal planning for monotonic tasks is NP-equivalent. We took a step towards a fine-grained classification of worst-case time complexity of optimal monotonic planning, with a focus on ``what gets harder" and ``what gets easier" when switching from optimal planning to optimal relaxed planning, in the context of finite-domain planning task representations.

The Complexity of Optimal Monotonic Planning: The Bad, The Good, and The Causal Graph

For almost two decades, monotonic, or ``delete free,'' relaxation has been one of the key auxiliary tools in the practice of domain-independent deterministic planning. In the particular contexts of both satisficing and optimal planning, it underlies most state-of-the-art heuristic functions. While satisficing planning for monotonic tasks is polynomial-time, optimal planning for monotonic tasks is NP-equivalent. Here we establish both negative and positive results on the complexity of some wide fragments of optimal monotonic planning, with the fragments being defined around the causal graph topology. Our results shed some light on the link between the complexity of general optimal planning and the complexity of optimal planning for the respective monotonic relaxations.

Improving Perceived Fairness of Task Assignments in Cardiovascular Intensive Nursing Care Unit with Simple Queuing Mechanism

In this work, a simple computer application is proposed to improve the perceived fairness of Cardiovascular Intensive Care Unit (CVICU) nursing team in hospitals. The proposed system called Intensive care Unit (ICU) Task Assignment Platform (ICU-TAP) is aimed to increase perceived fairness and satisfaction through a systematic but flexible scheduling method. The solution proposed here takes into account the skill preference of each nurse to ensure an equal opportunity to maintain career goals, with an aim to increasing job satisfaction. The system is designed based on the basic concept of queuing. ICU-TAP equally distributes the harder and monotonic tasks among nurses and takes into account the desired skill of each nurse for more frequent assignment of other tasks. The proposed system is expected to make the workflow of the unit traceable, resulting in paperless scheduling, and reducing discrepancies between scheduling and daily task assignment of the nursing unit.

Feature Selection for Monotonic Classification

Monotonic classification is a kind of special task in machine learning and pattern recognition. Monotonicity constraints between features and decision should be taken into account in these tasks. However, most existing techniques are not able to discover and represent the ordinal structures in monotonic datasets. Thus, they are inapplicable to monotonic classification. Feature selection has been proven effective in improving classification performance and avoiding overfitting. To the best of our knowledge, no technique has been specially designed to select features in monotonic classification until now. In this paper, we introduce a function, which is called rank mutual information, to evaluate monotonic consistency between features and decision in monotonic tasks. This function combines the advantages of dominance rough sets in reflecting ordinal structures and mutual information in terms of robustness. Then, rank mutual information is integrated with the search strategy of min-redundancy and max-relevance to compute optimal subsets of features. A collection of numerical experiments are given to show the effectiveness of the proposed technique.

Large-margin feature selection for monotonic classification

Monotonic classification plays an important role in the field of decision analysis, where decision values are ordered and the samples with better feature values should not be classified into a worse class. The monotonic classification tasks seem conceptually simple, but difficult to utilize and explain the order structure in practice. In this work, we discuss the issue of feature selection under the monotonicity constraint based on the principle of large margin. By introducing the monotonicity constraint into existing margin based feature selection algorithms, we design two new evaluation algorithms for monotonic classification. The proposed algorithms are tested with some artificial and real data sets, and the experimental results show its effectiveness.

Exact scheduling analysis of non-accumulatively monotonic multiframe tasks

In this paper, we present exact analysis for the worst case response time of the general multiframe (MF) task model executing on a uniprocessor according to the fixed priority scheduling scheme. The analysis is developed in four stages. Firstly, we present the basic response time analysis where we optimize the number of frames that have to be considered in such analysis; we show how their number can be significantly reduced by eliminating non critical frames that are dominated by other frames. Secondly, we extend this analysis to be applicable to MF tasks with arbitrary deadlines. Thirdly, the basic analysis is extended to cope with frame specific deadlines. Lastly, the two models of frame specific deadlines and arbitrary deadlines are combined and the relative analysis is presented. An optimal priority assignment scheme for the frame specific deadline scenario is also presented in this paper.

A parallel monotonic classification machine

Current parallel machines, such as array processors and Prolog or Lisp machines, are not optimized to execute monotonic classification tasks. A new special-purpose parallel computer is proposed, which efficiently solves this important type of problem from examples it encounters. After the study of sufficient examples, the macine is capable of classifying objects which it might have never seen before. What makes the machine unique is its low gate count when compared with its counterparts.