Journal articles: 'Decision algorithm'

1

Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 13, 2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Antyufeev, S. V. "Fuzzy decision algorithm." Programming and Computer Software 32, no. 6 (December 2006): 317–23. http://dx.doi.org/10.1134/s0361768806060041.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ma, Xiuqin, Yanan Wang, Hongwu Qin, and Jin Wang. "A Decision-Making Algorithm Based on the Average Table and Antitheses Table for Interval-Valued Fuzzy Soft Set." Symmetry 12, no. 7 (July 7, 2020): 1131. http://dx.doi.org/10.3390/sym12071131.

Full text

Abstract:

Interval-valued fuzzy soft set is one efficient mathematical model employed to handle the uncertainty of data. At present, there exist two interval-valued fuzzy soft set-based decision-making algorithms. However, the two existing algorithms are not applicable in some cases. Therefore, for the purpose of working out this problem, we propose a new decision-making algorithm, based on the average table and the antitheses table, for this mathematical model. Here, the antitheses table has symmetry between the objects. At the same time, an example is designed to prove the availability of our algorithm. Later, we compare our proposed algorithm with the two existing decision-making algorithms in several cases. The comparison result shows that only our proposed algorithm can make an effective decision in exceptional cases, and the other two methods cannot make decisions. It is therefore obvious that our algorithm has a stronger decision-making ability, thus further demonstrating the feasibility of our algorithm. In addition, a real data set of the homestays in Siming District, Xiamen is provided to further corroborate the practicability of our algorithm in a realistic situation.

APA, Harvard, Vancouver, ISO, and other styles

4

Chen, Yi Rui, and Yi Zhuang. "An Adaptive Decision Concurrency Control Algorithm." Advanced Materials Research 1046 (October 2014): 512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.512.

Full text

Abstract:

For the lack of adaptability about the existing concurrency control algorithms, adaptive decision concurrency control algorithm is proposed. ADCC algorithm divides concurrency control process into two phases in: execution authorizing phase and strategy selecting phase. In execution authorizing phase, algorithm compares statistics and effectiveness of transactions to determine the execution order of conflict transactions. In strategy selecting phase, according to transactions’ read/write status and current conflict rate, algorithm selects optimistic/pessimistic conflict resolution strategy adaptively. Such selection mechanism makes ADCC algorithm have high efficiency no matter database system is busy or idle. Simulation experiment proves that ADCC algorithm this paper proposed is superior to classical strict two phases locking algorithm and hybrid concurrency control. So ADCC algorithm performs well in the period of concurrency control.

APA, Harvard, Vancouver, ISO, and other styles

5

Green, Ben, and Yiling Chen. "Algorithm-in-the-Loop Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13663–64. http://dx.doi.org/10.1609/aaai.v34i09.7115.

Full text

Abstract:

We introduce a new framework for conceiving of and studying algorithms that are deployed to aid human decision making: “algorithm-in-the-loop” systems. The algorithm-in-the-loop framework centers human decision making, providing a more precise lens for studying the social impacts of algorithmic decision making aids. We report on two experiments that evaluate algorithm-in-the-loop decision making and find significant limits to these systems.

APA, Harvard, Vancouver, ISO, and other styles

6

Chen, Li Fang, and Ying Ma. "Improved Algorithm for Discretization of Decision Table." Advanced Materials Research 532-533 (June 2012): 1649–53. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1649.

Full text

Abstract:

Discretization of decision table is the important step for pretreatment of data mining and machine learning, which related to the effect of learning. It has great contribution to speeding up the followed learning algorithms, cutting down the real demand of algorithms on running space and time. In this paper, the basic characteristics and framework of discretization approaches about greedy and improved algorithm are analyzed at first, then a new algorithm is put forward to select the useful cuts. The example is given to show that the useful cuts is consistent with the result of technicist. The algorithm offered the important theoretics basis for followed attribute reduction.

APA, Harvard, Vancouver, ISO, and other styles

7

Jin, Xiaomin, Zhongmin Wang, and Wenqiang Hua. "Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing." Mobile Information Systems 2019 (September 17, 2019): 1–17. http://dx.doi.org/10.1155/2019/8049804.

Full text

Abstract:

Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

APA, Harvard, Vancouver, ISO, and other styles

8

PURDILA, V., and S. G. PENTIUC. "Fast Decision Tree Algorithm." Advances in Electrical and Computer Engineering 14, no. 1 (2014): 65–68. http://dx.doi.org/10.4316/aece.2014.01010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Khuri, Sami, and Aida Batarekh. "A binary decision algorithm." Information Sciences 53, no. 3 (February 1991): 251–70. http://dx.doi.org/10.1016/0020-0255(91)90039-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Go, Eunby, Seungmin Lee, and Taeseon Yoon. "Analysis of Ebolavirus with Decision Tree and Apriori algorithm." International Journal of Machine Learning and Computing 4, no. 6 (2014): 543–46. http://dx.doi.org/10.7763/ijmlc.2014.v6.470.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (November 27, 2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.

Full text

Abstract:

In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.Â Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.

APA, Harvard, Vancouver, ISO, and other styles

13

Polgár, Zsolt Alfréd, Andrei Ciprian Hosu, Zsuzsanna Ilona Kiss, and Mihály Varga. "Vertical Handover Decision Algorithm for Heterogeneous Cellular-WLAN Networks." MACRo 2015 1, no. 1 (March 1, 2015): 1–12. http://dx.doi.org/10.1515/macro-2015-0001.

Full text

Abstract:

AbstractMulti-access and heterogeneous wireless communications are considered to be one of the solutions for providing generalized mobility, high system efficiency and improved user experience, which are important characteristics of the Next Generation Networks. This paper proposes a Vertical Handover (VHO) decision algorithm for heterogeneous network architectures which integrate both cellular networks and Wireless Local Area Networks (WLANs). The cellular-WLAN and WLAN-WLAN VHO decisions are taken based on parameters which characterize both the coverage and the traffic load of the WLANs. Computer simulations performed in complex scenarios show that the proposed algorithm ensures better performance compared to “classical” VHO decision algorithms.

APA, Harvard, Vancouver, ISO, and other styles

14

Kalech, Meir, and Shulamit Reches. "Decision Making with Dynamic Uncertain Events." Journal of Artificial Intelligence Research 54 (November 1, 2015): 233–75. http://dx.doi.org/10.1613/jair.4869.

Full text

Abstract:

When to make a decision is a key question in decision making problems characterized by uncertainty. In this paper we deal with decision making in environments where information arrives dynamically. We address the tradeoff between waiting and stopping strategies. On the one hand, waiting to obtain more information reduces uncertainty, but it comes with a cost. Stopping and making a decision based on an expected utility reduces the cost of waiting, but the decision is based on uncertain information. We propose an optimal algorithm and two approximation algorithms. We prove that one approximation is optimistic - waits at least as long as the optimal algorithm, while the other is pessimistic - stops not later than the optimal algorithm. We evaluate our algorithms theoretically and empirically and show that the quality of the decision in both approximations is near-optimal and much faster than the optimal algorithm. Also, we can conclude from the experiments that the cost function is a key factor to chose the most effective algorithm.

APA, Harvard, Vancouver, ISO, and other styles

15

Simoes, Marcelo Godoy. "Compensatory Multicriteria Aggregation Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 4 (August 20, 1999): 289–98. http://dx.doi.org/10.20965/jaciii.1999.p0289.

Full text

Abstract:

Performance aggregation in decision processes is easy to implement, amplified by clarity with varying degrees of compensation that are a measure of the decision-maker’s willingness to consider all goals and constraints globally. We extend this concept, suggesting that the notion of compensation should be applied to another step of the decision process. We presented a mathematical function for this -- the DI-operator - that complies with the notion of degrees of compensation. A query interface is used with a personal computer and the algorithm is applied in purchase decisions of mechanical materials, partner selection, and urban vehicle delivery scheduling.

APA, Harvard, Vancouver, ISO, and other styles

16

VUKIĆEVIĆ, MILAN, MILOŠ JOVANOVIĆ, BORIS DELIBAŠIĆ, SONJA IŠLJAMOVIĆ, and MILIJA SUKNOVIĆ. "REUSABLE COMPONENT-BASED ARCHITECTURE FOR DECISION TREE ALGORITHM DESIGN." International Journal on Artificial Intelligence Tools 21, no. 05 (October 2012): 1250022. http://dx.doi.org/10.1142/s0218213012500224.

Full text

Abstract:

Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been analyzed thoroughly. Reusable component design of decision tree algorithms has been recently suggested as a potential solution to these problems. In this paper we describe an architecture for component-based (white-box) decision tree algorithm design, and we present an open-source framework which enables design and fair testing of decision tree algorithms and their parts. This architecture and developed platform can provide the research community with a common codebase for storing, designing, and evaluating decision tree algorithms (traditional, multivariate and hybrid) and their partial improvements. It is intended for data mining practitioners, algorithm and software developers, and as well for students, as a technology enhanced learning tool.

APA, Harvard, Vancouver, ISO, and other styles

17

EL-GHAMRAWY, SALLY M., and ALI I. ELDESOUKY. "AN AGENT DECISION SUPPORT MODULE BASED ON GRANULAR ROUGH MODEL." International Journal of Information Technology & Decision Making 11, no. 04 (July 2012): 793–820. http://dx.doi.org/10.1142/s0219622012500216.

Full text

Abstract:

A multi-agent system (MAS) is a branch of distributed artificial intelligence, composed of a number of distributed and autonomous agents. In a MAS, effective coordination is essential for autonomous agents to achieve their goals. Any decision based on a foundation of knowledge and reasoning can lead agents into successful cooperation; to achieve the necessary degree of flexibility in coordination, an agent must decide when to coordinate and which coordination mechanism to use. The performance of any MAS depends directly on the decisions made by the agents. The agents must therefore be able to make correct decisions. This paper proposes a decision support module in a distributed MAS that is concerned with two main decisions: the decision needed to allocate a task to specific agent/s and the decision needed to select the appropriate coordination mechanism when agents must coordinate with other agent/s to accomplish a specific task. An algorithm for the task allocation decision maker (TADM) and the coordination mechanism selection decision maker (CMSDM) algorithm are proposed that are based on the granular rough model (GRM). Furthermore, a number of experiments were performed to validate the effectiveness of the proposed algorithms; the efficiency of the proposed algorithms is compared with recent works. The preliminary results demonstrate the efficiency of our algorithms.

APA, Harvard, Vancouver, ISO, and other styles

18

De Capua, Claudio, Rosario Morello, and Rosario Carbone. "Measurement Uncertainty in Decision-Making." International Journal of Measurement Technologies and Instrumentation Engineering 1, no. 3 (July 2011): 40–52. http://dx.doi.org/10.4018/ijmtie.2011070104.

Full text

Abstract:

In this paper, the authors examine a common issue concerning the influence of measurement uncertainty on decisions. In fact, in some practical applications, it can be necessary to put in comparison measurement data with thresholds and limits. It occurs when the conformity with fixed specifications has to be verified or if warning and alert levels have to be not exceeded. In such a circumstance, to take reliable decisions in presence of uncertainty is a concrete problem. Measurement uncertainty may reasonably be the cause of unreliable decisions. In order to manage properly the uncertainty effect, the authors have developed a decision making procedure based on a methodical approach to measurement uncertainty. In detail, a fuzzy logic algorithm estimates the probability to take a wrong decision because of the uncertainty. Such information is so used in order to optimize the decisional criteria, improving the consistency of the final computing results. Risks and costs associated to the possibility to take a mistaken decision are minimized. Consequently the algorithm singles out the most reliable decision.

APA, Harvard, Vancouver, ISO, and other styles

19

Lu, Guangyan, and Wenjun Chang. "Comparative analysis of evolutionary algorithms for multiple criteria decision making with interval-valued belief distributions." Intelligent Decision Technologies 14, no. 3 (September 29, 2020): 373–91. http://dx.doi.org/10.3233/idt-190125.

Full text

Abstract:

In multiple criteria decision making (MCDM) with interval-valued belief distributions (IVBDs), individual IVBDs on multiple criteria are combined explicitly or implicitly to generate the expected utilities of alternatives, which can be used to make decisions with the aid of decision rules. To analyze an MCDM problem with a large number of criteria and grades used to profile IVBDs, effective algorithms are required to find the solutions to the optimization models within a large feasible region. An important issue is to identify an algorithm suitable for finding accurate solutions within a limited or acceptable time. To address this issue, four representative evolutionary algorithms, including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, and gravitational search algorithm, are selected to combine individual IVBDs of alternatives and generate the minimum and maximum expected utilities of alternatives. By performing experiments with different numbers of criteria and grades, a comparative analysis of the four algorithms is provided with the aid of two indicators: accuracy and efficiency. Experimental results indicate that particle swarm optimization algorithm is the best among the four algorithms for combining individual IVBDs and generating the minimum and maximum expected utilities of alternatives.

APA, Harvard, Vancouver, ISO, and other styles

20

Mazraeh, Saeed, Maryam Ghanavati, and Sajedeh Hasan Nejad Neysi. "Intrusion detection system with decision tree and combine method algorithm." International Academic Journal of Science and Engineering 06, no. 01 (June 4, 2019): 167–77. http://dx.doi.org/10.9756/iajse/v6i1/1910016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Ã–zsoy, Salih, GÃ¶khan GÃ¼mÃ¼ÅŸ, and Savriddin KHALILOV. "C4.5 Versus Other Decision Trees: A Review." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 173–82. http://dx.doi.org/10.18495/comengapp.v4i3.141.

Full text

Abstract:

In this study, Data Mining, one of the latest technologies of the Information Systems, was introduced and Classification a Data Mining method and the Classification algorithms were discussed. A classification was applied by using C4.5 decision tree algorithm on a dataset about Labor Relations from http://archive.ics.uci.edu/ml/datasets.html. Finally, C4.5 algorithm was compared to some other decision tree algorithms. C4.5 was the one of the successful classifier.

APA, Harvard, Vancouver, ISO, and other styles

22

Kleinberg, Jon, and Manish Raghavan. "Algorithmic monoculture and social welfare." Proceedings of the National Academy of Sciences 118, no. 22 (May 25, 2021): e2018340118. http://dx.doi.org/10.1073/pnas.2018340118.

Full text

Abstract:

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.

APA, Harvard, Vancouver, ISO, and other styles

23

Namatevs, Ivars, and Ludmila Aleksejeva. "Decision Algorithm for Heuristic Donor-Recipient Matching." MENDEL 23, no. 1 (June 1, 2017): 33–40. http://dx.doi.org/10.13164/mendel.2017.1.033.

Full text

Abstract:

This paper introduces the application of artificial intelligence paradigm towards precision medicine in renal transplantation. The match of the optimal donor-recipient pair in kidney transplantation in Latvian Transplant Centre (LTC) has been constrained by the lack of prediction models and algorithms. Consequently, LTC seeks for practical intelligent computing solution to assist the clinical setting decision-makers during their search for the optimal donor-recipient match. Therefore, by optimizing both the donor and recipient profiles, prioritizing importance of the features, and based on greedy algorithm approach, advanced decision algorithm has been created. The strength of proposed algorithm lies in identification of suitable donors for a specific recipient based on evaluation of criteria by points principle. Experimental study demonstrates that the decision algorithm for heuristic donor-recipient matching integrated in machine learning approach improves the ability of optimal allocation of renal in LTC. It is an important step towards personalized medicine in clinical settings.

APA, Harvard, Vancouver, ISO, and other styles

24

Reid, Chris R., Hannelore MacDonald, Richard P. Mann, James A. R. Marshall, Tanya Latty, and Simon Garnier. "Decision-making without a brain: how an amoeboid organism solves the two-armed bandit." Journal of The Royal Society Interface 13, no. 119 (June 2016): 20160030. http://dx.doi.org/10.1098/rsif.2016.0030.

Full text

Abstract:

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems.

APA, Harvard, Vancouver, ISO, and other styles

25

Gruszecki, Jan, and Fatina Liliana Basmadji. "ROUGH PILOT DECISION‐SUPPORT ALGORITHM." Aviation 11, no. 3 (September 30, 2007): 31–36. http://dx.doi.org/10.3846/16487788.2007.9635967.

Full text

Abstract:

In this paper, a new application for the theory of rough sets is proposed. The theory of rough sets has been introduced into one of the most complicated fields, military aviation. The problem is to generate a pilot's decision‐support algorithm for the task of dropping a bomb on a target located behind an obstacle detected during flight. The aim of this algorithm is to simultaneously facilitate the performance of this task for the pilot; it will minimize pilot error caused by imperfect accuracy in estimating the situation and limited experience in a given situation.

APA, Harvard, Vancouver, ISO, and other styles

26

Chandra, B., and P. P. Varghese. "Fuzzy SLIQ Decision Tree Algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (October 2008): 1294–301. http://dx.doi.org/10.1109/tsmcb.2008.923529.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Gabaix, Xavier, and David Laibson. "A Boundedly Rational Decision Algorithm." American Economic Review 90, no. 2 (May 1, 2000): 433–38. http://dx.doi.org/10.1257/aer.90.2.433.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Ji, Xue, Qi Gao, Fupeng Yin, and Hengdong Guo. "An Efficient Imperialist Competitive Algorithm for Solving the QFD Decision Problem." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2601561.

Full text

Abstract:

It is an important QFD decision problem to determine the engineering characteristics and their corresponding actual fulfillment levels. With the increasing complexity of actual engineering problems, the corresponding QFD matrixes become much huger, and the time spent on analyzing these matrixes and making decisions will be unacceptable. In this paper, a solution for efficiently solving the QFD decision problem is proposed. The QFD decision problem is reformulated as a mixed integer nonlinear programming (MINLP) model, which aims to maximize overall customer satisfaction with the consideration of the enterprises’ capability, cost, and resource constraints. And then an improved algorithm G-ICA, a combination of Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA), is proposed to tackle this model. The G-ICA is compared with other mature algorithms by solving 7 numerical MINLP problems and 4 adapted QFD decision problems with different scales. The results verify a satisfied global optimization performance and time performance of the G-ICA. Meanwhile, the proposed algorithm’s better capabilities to guarantee decision-making accuracy and efficiency are also proved.

APA, Harvard, Vancouver, ISO, and other styles

29

Gao, Jing. "Decision Tree Generation Algorithm without Pruning." Applied Mechanics and Materials 441 (December 2013): 731–37. http://dx.doi.org/10.4028/www.scientific.net/amm.441.731.

Full text

Abstract:

On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying and predicting the coming data. To settle these problems, the restrained factor is introduced in this paper. For expanding nodes in generating decision tree algorithm, besides traditional terminating condition, an additional terminating condition is involved when the restrained factor of sample is higher than a given threshold, then the node will not be expanded any more. Thus, the problem of much more meticulous partition is avoided. Furthermore, the size of decision tree generated with restrained factor involved will not seem too large to be understood.

APA, Harvard, Vancouver, ISO, and other styles

30

Deepa, N., B. Prabadevi, and Gautam Srivastava. "Integrated Ranking Algorithm for Efficient Decision Making." International Journal of Information Technology & Decision Making 20, no. 02 (February 26, 2021): 597–618. http://dx.doi.org/10.1142/s0219622021500152.

Full text

Abstract:

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

APA, Harvard, Vancouver, ISO, and other styles

31

Tometzki, Thomas, and Sebastian Engell. "Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation." Evolutionary Computation 17, no. 4 (December 2009): 511–26. http://dx.doi.org/10.1162/evco.2009.17.4.17404.

Full text

Abstract:

In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

APA, Harvard, Vancouver, ISO, and other styles

32

Jin, Maozhu, Hua Wang, Qian Zhang, and Cheng Luo. "Financial Management and Decision Based on Decision Tree Algorithm." Wireless Personal Communications 102, no. 4 (February 5, 2018): 2869–84. http://dx.doi.org/10.1007/s11277-018-5312-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Guo, Dan. "Application of Decision Tree Algorithm in Lumber Hierarchies." Advanced Materials Research 466-467 (February 2012): 308–13. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.308.

Full text

Abstract:

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

APA, Harvard, Vancouver, ISO, and other styles

34

Shayea, Ibraheem, Mahamod Ismail, Rosdiadee Nordin, and Hafizal Mohamad. "Adaptive Handover Decision Algorithm Based on Multi-Influence Factors through Carrier Aggregation Implementation in LTE-Advanced System." Journal of Computer Networks and Communications 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/739504.

Full text

Abstract:

Although Long Term Evolution Advanced (LTE-Advanced) system has benefited from Carrier Aggregation (CA) technology, the advent of CA technology has increased handover scenario probability through user mobility. That leads to a user’s throughput degradation and its outage probability. Therefore, a handover decision algorithm must be designed properly in order to contribute effectively for reducing this phenomenon. In this paper, Multi-Influence Factors for Adaptive Handover Decision Algorithm (MIF-AHODA) have been proposed through CA implementation in LTE-Advanced system. MIF-AHODA adaptively makes handover decisions based on different decision algorithms, which are selected based on the handover scenario type and resource availability. Simulation results show that MIF-AHODA enhances system performance better than the other considered algorithms from the literature by 8.3 dB, 46%, and 51% as average gains over all the considered algorithms in terms of SINR, cell-edge spectral efficiency, and outage probability reduction, respectively.

APA, Harvard, Vancouver, ISO, and other styles

35

Sameer, S. K. L., and P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 29, 2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.

Full text

Abstract:

Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.

APA, Harvard, Vancouver, ISO, and other styles

36

Dinesh, T. "Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 1084–96. http://dx.doi.org/10.47059/revistageintec.v11i2.1738.

Full text

Abstract:

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

APA, Harvard, Vancouver, ISO, and other styles

37

Damanik, Irfan Sudahri, Agus Perdana Windarto, Anjar Wanto, Poningsih, Sundari Retno Andani, and Widodo Saputra. "Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm." Journal of Physics: Conference Series 1255 (August 2019): 012012. http://dx.doi.org/10.1088/1742-6596/1255/1/012012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

ZHAO, S. J. "CONSTRUCTION OF ENTERPRISE ECONOMIC DECISION RECOMMENDATION SYSTEM BASED ON COMBINED ASSOCIATION ANALYSIS MODEL." Latin American Applied Research - An international journal 48, no. 4 (October 31, 2018): 249–54. http://dx.doi.org/10.52292/j.laar.2018.236.

Full text

Abstract:

With the development of the information age and the network economy, enterprises need more and more decisions, and the difficulty and complexity of decision-making are constantly improving. The traditional centralized decision-making is no longer in line with the requirements of current social and economic development. Therefore, based on the in-depth study of conventional algorithms, this paper constructs a portfolio association analysis model for enterprise economic decision-making recommendation system, and uses the off-line test method to test the construction model. The test results show that the accuracy and recall rate calculated by the combination algorithm are higher than the common collaborative filtering algorithm. The enterprise economic decision recommendation system realizes the integration of economic mathematical model, operational research method and decision-making means, greatly improving the enterprise. The accuracy of scientifically feasible decision-making has improved the efficiency of business operations to a certain extent.

APA, Harvard, Vancouver, ISO, and other styles

39

Vorobeichikova, O. V. "APPLICATION OF COMPUTER TECHNOLOGIES IN TEACHING OF MEDICAL STUDENTS." Bulletin of Siberian Medicine 13, no. 4 (August 28, 2014): 27–31. http://dx.doi.org/10.20538/1682-0363-2014-4-27-31.

Full text

Abstract:

he purpose of the given research are situational tasks from the point of view of algorithms of their decision and application of computer technologies for realization of similar algorithms. In the beginning the concept of a situational task and an opportunity of their use for training medical students is considered. The analysis of existing situational clinical tasks is spent and classification of algorithms of the decision is resulted. The opportunity of application of computer technologies for realization of similar algorithms is considered. Among all existing algorithms of the decision one in which the algorithm can be applied to the decision of the same tasks of one class is especially allocated. The technology of construction of such algorithm is resulted and the description of a program complex which realizes such algorithm of the decision of situational tasks is given.

APA, Harvard, Vancouver, ISO, and other styles

40

Shih, Andy, Arthur Choi, and Adnan Darwiche. "Compiling Bayesian Network Classifiers into Decision Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7966–74. http://dx.doi.org/10.1609/aaai.v33i01.33017966.

Full text

Abstract:

We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers. In particular, we compile Bayesian network classifiers into ordered decision graphs, which are tractable and can be exponentially smaller in size than decision trees. This tractability facilitates reasoning about the behavior of Bayesian network classifiers, including the explanation of decisions they make. Our compilation algorithm comes with guarantees on the time of compilation and the size of compiled decision graphs. We apply our compilation algorithm to classifiers from the literature and discuss some case studies in which we show how to automatically explain their decisions and verify properties of their behavior.

APA, Harvard, Vancouver, ISO, and other styles

41

García-Martín, Eva, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, and Veselka Boeva. "Energy-aware very fast decision tree." International Journal of Data Science and Analytics 11, no. 2 (March 2021): 105–26. http://dx.doi.org/10.1007/s41060-021-00246-4.

Full text

Abstract:

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

APA, Harvard, Vancouver, ISO, and other styles

42

Kim, Taehwan, and Taeseon Yoon. "Artificial Neural Network Hybrid Algorithm Combimed with Decision Tree and Table." International Journal of Machine Learning and Computing 5, no. 6 (December 2015): 471–75. http://dx.doi.org/10.18178/ijmlc.2015.5.6.555.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

BHATT, RAJEN B., and M. GOPAL. "NEURO-FUZZY DECISION TREES." International Journal of Neural Systems 16, no. 01 (February 2006): 63–78. http://dx.doi.org/10.1142/s0129065706000470.

Full text

Abstract:

Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.

APA, Harvard, Vancouver, ISO, and other styles

44

López-Morales, Virgilio, and Joel Suárez-Cansino. "Reliable Intervals Method in Decision-Based Support Models for Group Decision-Making." International Journal of Information Technology & Decision Making 16, no. 01 (January 2017): 183–204. http://dx.doi.org/10.1142/s0219622016500498.

Full text

Abstract:

In this paper, a methodology to derive reliable intervals for multiplicative preference relations (or pairwise comparison matrices) satisfying consistency and consensus indexes is introduced. Our approach is proposed via a combination of numerical algorithms and a nonlinear optimization algorithm. A synthesis of reliable intervals is achieved, where group decision makers show evidence of these intervals to express flexibility in the manner of their preferences, while accomplishing some a priori decision targets, rules and advice given by their current framework. The algorithms are applied to some examples in order to illustrate our results and compare them with other methodologies.

APA, Harvard, Vancouver, ISO, and other styles

45

Hoang, Quang Minh, Vu Duc Thi, and Nguyen Ngoc San. "Some algorithms related to consistent decision table." Journal of Computer Science and Cybernetics 33, no. 2 (December 29, 2017): 131–42. http://dx.doi.org/10.15625/1813-9663/33/2/9281.

Full text

Abstract:

Rough set theory is useful mathematical tool developed to deal with vagueness and uncertainty. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. Rough set theory is also the most popular for generating decision rules from decision table. In this paper, we propose an algorithm finding object reduct of consistent decsion table. On the other hand, we also show an algorithm to find some attribute reducts and the correctness of our algorithms is proof-theoretically. These our algorithms have polynomial time complexity. Our finding object reduct helps other algorithms of finding attribute reducts become more effectively, especially as working with huge consistent decision table.

APA, Harvard, Vancouver, ISO, and other styles

46

Gräßer, Felix, Hagen Malberg, and Sebastian Zaunseder. "Neighborhood Optimization for Therapy Decision Support." Current Directions in Biomedical Engineering 5, no. 1 (September 1, 2019): 1–4. http://dx.doi.org/10.1515/cdbme-2019-0001.

Full text

Abstract:

AbstractThis work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.

APA, Harvard, Vancouver, ISO, and other styles

47

Suyadi, Suyadi, Arief Setyanto, and Hanif Al Fattah. "Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas." Indonesian Journal of Applied Informatics 2, no. 1 (December 16, 2017): 59. http://dx.doi.org/10.20961/ijai.v2i1.13258.

Full text

Abstract:

<em>Profile of PMB (New Student Admissions) students from several periods have abundant data that can be used for research. The data is in the form of student information from the majors of origin, NEM and majors now. Classifying the PMB profile data of students at the University level in Yogyakarta can know the majority of learners. Comparing some algorithms is needed to find out the best algorithm. Classification is a grouping algorithm that has several algorithms such as Decision Tree (C4.5) and K-Naive Bayes. Decision Tree (C4.5) is an algorithm with decision tree, while K-Naive Bayes is the likely algorithm that will occur. This analysis uses Rapidminer which is a data analysis software with features of several algorithms that are easy to operate. Both algorithms have results with large data of 1504 students, Decision tree (C4.5) has an accuracy of 81.84% and an error accuracy of 18.16%, while K-Naive Bayes 85.12% and accuracy of error 14.88%. Whereas with smaller data the Decision tree (C4.5) has 100% accuracy whereas K-Naive Bayes has the same accuracy as Decision Tree (C4.5) that is 100%.</em>

APA, Harvard, Vancouver, ISO, and other styles

48

Xu, Shi Jun, Li Hong, and Yong Hong Hu. "A Distributed Bayesian Fusion Algorithm Research." Advanced Materials Research 181-182 (January 2011): 1006–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1006.

Full text

Abstract:

In this paper, the signal detection problem when distributed sensors are used a global decision is desired is considered. Local decisions from the sensors are fed to the data fusion center which then yields a global decision based on a fusion rule. Based on The data fusion theories of Bayesian criterion used for a distributed parallel structure, fusion rules at the fusion center、 the decision rules of sensors and the results of the computer simulation for two identical sensors, two different sensors and three identical sensors are presented. The results of the computer simulation show that the performance of the fusion system, compared with the sensor, has been improved. For the case there are three identical sensors in the fusion system, Bayesian risk is reduced by 26.5%, compared with the sensor.

APA, Harvard, Vancouver, ISO, and other styles

49

Lebedev, B. K., O. B. Lebedev, and A. A. Zhiglaty. "Binary Decision Tree Construction using the Hybrid Swarm Intelligence." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 2 (135) (June 2021): 52–65. http://dx.doi.org/10.18698/0236-3933-2021-2-52-65.

Full text

Abstract:

Solving the problem of a classification model construction is presented in the form of a sequence of considered attributes and values thereof included in the Mk route from the root to the dangling vertex. Decision tree developed interpretation is presented as a pair of chromosomes (Sk, Wk). The Sk chromosome list of genes corresponds to the list of all attributes included in the Mk route in the decision tree. The Wk chromosome gene values correspond to the attribute values included in the Mk route. Unification of data structures, search space and modernization of integrable algorithms was carried out for hybridization. Hybrid algorithm operators are using the integer parameters and synthesize new integer parameter values. Method was developed to account for simultaneous attraction of the αi particle to three xi (t), x*i (t), x*(t) attractors dislocating from the xi (t) position to the xi (t + 1) position. Modified hybrid metaheuristic of the search algorithm is proposed for constructing a classification model using recombination of swarm and genetic search algorithms. The first approach uses genetic algorithm initially and then the particle swarm algorithm. The second approach uses the high-level nesting hybridization method based on combination of genetic algorithm and particle swarm algorithm. The proposed approach to constructing a modified paradigm uses chromosomes with integer parameter values in the indicated hybrid algorithm and operators, which assist chromosomes to evolve according to the rules of particle swarm and genetic search

APA, Harvard, Vancouver, ISO, and other styles

50

Yusriski, Rinto, Budi Astuti, Damawijaya Biksono, and Tika Ayu Wardani. "A single machine multi-job integer batch scheduling problem with multi due date to minimize total actual flow time." Decision Science Letters 10, no. 3 (2021): 231–40. http://dx.doi.org/10.5267/j.dsl.2021.4.002.

Full text

Abstract:

This research deals with a multi-job Integer batch scheduling problem on a single machine with different due dates. Every job demanded one or more parts, and the single machine processed the job into a number of batches. The objective is to minimize total actual flow time, defined as the total flow time of all jobs starting from the arrival to the common due date. The decisions are to determine the sequence of jobs, the number of batches, batch size, and sequence of all batches on a single machine. This research proposes three algorithms, developed based on the longest due date rule (The P1-LDD Algorithm), the adjacent pairwise interchange method (The P2-API Algorithm), and the permutation method (The P3-PM Algorithm). The numerical experience shows that the three algorithms produce an outstanding solution. The P1-LDD Algorithm fits to solve a simple problem. The P2-API Algorithm has superior to solve a big complicated problem. The P3-PM Algorithm has the best performance to solve small complicated problems.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Decision algorithm'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles