Meta-Feature Based Data Mining Service Selection and Recommendation Using Machine Learning Models

Alghofaily, Bayan

doi:10.1109/icebe.2018.00014

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…A smart-device news recommendation technology based on the user click behavior [44] Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach [45] A novel approach towards context based recommendations using support vector machine methodology [46] A smartphone-based activity-aware system for music streaming recommendation [47] An app usage recommender system: improving prediction accuracy for both warm and cold start users [48] Proposing design recommendations for an intelligent recommender system logging stress [49] A recommender system based on implicit feedback for selective dissemination of eBooks [50] A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases [51] An approach to content based recommender systems using decision list based classification with k-DNF rule set [52] Probabilistic approach for QoS-aware recommender system for trustworthy web service selection [53] Approach to cold-start problem in recommender systems in the context of web-based education [54] Context and intention-awareness in POIs recommender systems [55] A collaborative filtering-based re-ranking strategy for search in digital libraries [56] Learning users' interests by quality classification in market-based recommender systems [57] Mobile content recommendation system for revisiting user using content-based filtering and clientside user profile [58] A hybrid collaborative filtering algorithm based on KNN and gradient boosting [59] A scalable collaborative filtering algorithm based on localized preference [60] Recommended or not recommended? Review classification through opinion extraction [61] Meta-feature based data mining service selection and recommendation using machine learning models [62] Personalized channel recommendation deep learning from a switch sequence [63] Affective labeling in a content-based recommender system for images [64] A novel approach towards context sensitive recommendations based on machine learning methodology [65] A distance-based approach for action recommendation [66] Ranking and classifying attractiveness of photos in folksonomies [67] Consequences of variability in classifier performance estimates [68] Machine learning ...…”

Section: Titlementioning

confidence: 99%

Recommendation and Classification Systems: A Systematic Mapping Study

Enríquez

Morales-Trujillo

Calle-Alonso

et al. 2019

Scientific Programming

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Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers. Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high. Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem. This is the case of the ADAGIO, a R&D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data. In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option. To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial. More than a thousand papers have been analyzed resulting in 80 primary studies. Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice. In fact, the validation presented for both cases is very scarce in the industrial environment. From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis. This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage. It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.

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Section: Titlementioning

confidence: 99%

Recommendation and Classification Systems: A Systematic Mapping Study

Enríquez

Morales-Trujillo

Calle-Alonso

et al. 2019

Scientific Programming

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show abstract

“…In turn, regression allows one to estimate muscle effort strength by its EMG signal and, hence, can be used for proportional (gradual) control, e.g., for reconstruction of torque value of some joints [6]. In addition to other mathematical tools, ANNs were also successfully applied to the regression problem, including multichannel registration [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Lobov

Chernyshov

Krilova

et al. 2020

Sensors

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One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

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Grocery Product Classification and Recommendation System Based on Machine Learning and Customer Profile Identity

Hanumantharaju

Murthy

2020

Lecture Notes in Electrical Engineering

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Meta-Feature Based Data Mining Service Selection and Recommendation Using Machine Learning Models

Cited by 4 publications

References 15 publications

Recommendation and Classification Systems: A Systematic Mapping Study

Recommendation and Classification Systems: A Systematic Mapping Study

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Grocery Product Classification and Recommendation System Based on Machine Learning and Customer Profile Identity

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