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نتیجه جستجو - Feature combination

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 A non-canonical hybrid metaheuristic approach to adaptive data stream classification
یک روش متاوریستی ترکیبی غیر متعارف برای طبقه بندی جریان داده تطبیقی-2020
Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) and global (in all types) optimums with a specified velocity. A set of experiments are conducted to compare the performance of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in immediate and delayed prequential evaluation settings. The results show a favourable performance of our method in different environments.
Keywords: Ensemble learning | Data stream mining | Concept drifts | Bio-inspired algorithms | Non-stationary environments | Particle swarm optimisation | Replicator dynamics
مقاله انگلیسی
2 Robust feature sets for contraction level invariant control of upperlimb myoelectric prosthesis
مجموعه ویژگی های قوی برای کنترل ثابت سطح انقباض پروتز میو الکتریک فوقانی-2019
In spite of the tremendous progress of upper limb myoelectric prosthetic control in the field of rehabili-tation engineering, there still exist several real world challenges to be met, before realizing it as a goodsubstitute for a natural arm. Incompetence of the system to accommodate variations in contraction levelsof muscle movements has been identified as one of the significant challenges, as these variations havea subsequent impact on the performance of pattern recognition based myoelectric control. Non-lineartechniques are more suited to characterize myoelectric signals since one of their major properties is non-linearity. Based on this we propose two feature combinations which can lead to a reliable control schemethat is robust against contraction level variations. The performance of our proposed features when testedon nine transradial amputees for six motion classes at three different force levels outweighed otherestablished feature extraction methods meant for contraction variation independent control. Significantimprovement of around 8% in average classification performance was achieved across all subjects andforce levels, subjected to training, both with all force levels and with unseen force levels. Moreover, thesefeatures achieved superior performance in classifying flexion as well as grip movements.
Keywords: Electromyogram | Myoelectric prosthesis |Pattern recognition | Contraction level variation | Fractal analysis | Entropya
مقاله انگلیسی
3 A hybrid approach to building face shape classifier for hairstyle recommender system
یک روش ترکیبی برای ساخت طبقه بندی فرم صورت برای سیستم توصیه کننده مدل مو-2019
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This frame- work enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Sup- port Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these indi- vidual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Keywords: Face shape classification | Deep-learned feature | Hand-crafted feature | Hybrid feature-based approach | Feature combination
مقاله انگلیسی
4 A hybrid approach to building face shape classifier for hairstyle recommender system
یک روش ترکیبی برای ساخت طبقه بندی فرم صورت برای سیستم توصیه کننده مدل مو-2019
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This frame- work enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Sup- port Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these indi- vidual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Keywords: Face shape classification | Deep-learned feature | Hand-crafted feature | Hybrid feature-based approach | Feature combination
مقاله انگلیسی
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