II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Iantovics, László Barna; Iakovidis, Dimitris K.; Nechita, Elena

doi:10.2991/ijcis.d.191101.001

Cited by 5 publications

(4 citation statements)

References 91 publications

(126 reference statements)

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“…As already pointed out in the introduction, in MCS systems, a major problem is the quality of the data. Inconsistent data could have a negative effect on the accuracy and performance of machine learning systems, even leading to a decrease in the intelligence of the system [31]. In [32], the authors deal with this problem by computing the reputation score of the device as a reflection of the trustworthiness of the contributed data.…”

Section: Discussionmentioning

confidence: 99%

Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition

et al. 2022

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Mobile Crowd Sensing (MCS) is a novel IoT paradigm where sensor data, as collected by the user’s mobile devices, are integrated with user-generated content, e.g., annotations, self-reports, or images. While providing many advantages, the human involvement also brings big challenges, where the most critical is possibly the poor quality of human-provided content, most often due to the inaccurate input from non-expert users. In this paper, we propose Skeptical Learning, an interactive machine learning algorithm where the machine checks the quality of the user feedback and tries to fix it when a problem arises. In this context, the user feedback consists of answers to machine generated questions, at times defined by the machine. The main idea is to integrate three core elements, which are (i) sensor data , (ii) user answers, and (iii) existing prior knowledge of the world, and to enable a second round of validation with the user any time these three types of information jointly generate an inconsistency. The proposed solution is evaluated in a project focusing on a university student life scenario. The main goal of the project is to recognize the locations and transportation modes of the students. The results highlight an unexpectedly high pervasiveness of user mistakes in the university students life project. The results also shows the advantages provided by Skeptical Learning in dealing with the mislabeling issues in an interactive way and improving the prediction performance.

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Section: Discussionmentioning

confidence: 99%

Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition

et al. 2022

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“…In future research, in order to better analyze the target classification performance for long distance and clutter background, the coherent pulse-Doppler radar will be used and the target's characteristics, e.g., m-D, will be investigated. Further, the CNN may be more intelligent with the intelligent systems, which enables truly intelligent processing and recognition [31].…”

Section: Discussionmentioning

confidence: 99%

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

et al. 2022

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Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

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“…These results indicate that the hybrid reinforcement learning algorithm of HDRL-Trader with its novel techniques is very effective for generalization. The experimental results can be statistically analyzed using the metrics for measuring machine intelligence [54], and we leave this as future work.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Hybrid Deep Reinforcement Learning for Pairs Trading

2022

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Pairs trading is an investment strategy that exploits the short-term price difference (spread) between two co-moving stocks. Recently, pairs trading methods based on deep reinforcement learning have yielded promising results. These methods can be classified into two approaches: (1) indirectly determining trading actions based on trading and stop-loss boundaries and (2) directly determining trading actions based on the spread. In the former approach, the trading boundary is completely dependent on the stop-loss boundary, which is certainly not optimal. In the latter approach, there is a risk of significant loss because of the absence of a stop-loss boundary. To overcome the disadvantages of the two approaches, we propose a hybrid deep reinforcement learning method for pairs trading called HDRL-Trader, which employs two independent reinforcement learning networks; one for determining trading actions and the other for determining stop-loss boundaries. Furthermore, HDRL-Trader incorporates novel techniques, such as dimensionality reduction, clustering, regression, behavior cloning, prioritized experience replay, and dynamic delay, into its architecture. The performance of HDRL-Trader is compared with the state-of-the-art reinforcement learning methods for pairs trading (P-DDQN, PTDQN, and P-Trader). The experimental results for twenty stock pairs in the Standard & Poor’s 500 index show that HDRL-Trader achieves an average return rate of 82.4%, which is 25.7%P higher than that of the second-best method, and yields significantly positive return rates for all stock pairs.

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II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Cited by 5 publications

References 91 publications

Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition

Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

Hybrid Deep Reinforcement Learning for Pairs Trading

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