An Artificial Immune Classification Algorithm based on Particle Swarm Optimization

Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm was proposed to learn opponent's negotiation preference. The process of negotiation was viewed as a proposal's sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semisupervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent's negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.

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“…Counter proposal is expressed as b=(b 1 ,b 2 ,…,b Hypothesis space. We use PSO algorithm [17][18][19] to solve the optimization problem.…”

Section: Negotiation Counter-proposal Generationmentioning

confidence: 99%

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Zheng

Peng

2014

JSW

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“…Swarm intelligence algorithm is an efficient method to solve the global optimization problems. It mainly includes particle swarm optimization (PSO) [1], artificial immune (AI) [2], genetic algorithm (GA) [3], differential evolution (DE) [4], invasive weed optimization (IWO) [5] and so on.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Cuckoo Search Algorithm for High-dimensional Functions

Ouyang¹,

Guo²,

Yue³

et al. 2014

JCP

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Given that the basic cuckoo search algorithm is vulnerable to local optimum and unsatisfactory calculation precision, chaotic operator (CO), employed as local algorism to optimize elitist individuals in the population, can effectively enhance the properties of cuckoo search (CS) algorithm. Tested by 15 benchmark high-dimensional functions, the experiment result indicates that chaotic cuckoo search (CCS) algorithm can increase the calculation precision and step up the convergent speed with improved robustness, which can be applied to other engineering optimization problems.

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“…Immune systems regulate defence mechanism of innate and adaptive immune response. The latter is more important as it has metaphors like diversity, recognition, memory acquisition and self-regulation [23]. Of various mechanisms in a biological immune system that are explored, clonal selection, negative election and immune network model are most discussed.AIS' key features like feature extraction, recognition, and learning are used in classification and clustering tasks.…”

Section: Artificial Immune System (Ais)mentioning

confidence: 99%

BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

Chitra¹,

Karthikeyan²

2017

IJSC

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Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment's like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.

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An Artificial Immune Classification Algorithm based on Particle Swarm Optimization

Cited by 3 publications

References 18 publications

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Chaotic Cuckoo Search Algorithm for High-dimensional Functions

BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

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