ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6X and 6.8X, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature computation cost by 5.1X. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding

Multiclass optimal classification trees with SVM‑splits

In this paper we present a novel mathematical optimization-based methodology to construct tree-shaped classification rules for multiclass instances. Our approach consists of building Classification Trees in which, except for the leaf nodes, the labels are temporarily left out and grouped into two classes by means of a SVM separating hyperplane. We provide a Mixed Integer Non Linear Programming formulation for the problem and report the results of an extended battery of computational experiments to assess the performance of our proposal with respect to other benchmarking classification methods.Universidad de Sevilla/CBUASpanish Ministerio de Ciencia y Tecnología, Agencia Estatal de Investigación, and Fondos Europeos de Desarrollo Regional (FEDER) via project PID2020-114594GB-C21Junta de Andalucía projects FEDER-US-1256951, P18-FR-1422, CEI-3-FQM331, B-FQM-322-UGR20AT 21_00032; Fundación BBVA through project NetmeetData: Big Data 2019UE-NextGenerationEU (ayudas de movilidad para la recualificación del profesorado universitario)IMAG-Maria de Maeztu grant CEX2020- 001105-M /AEI /10.13039/50110001103

An ANFIS-based compatibility scorecard for IoT integration in websites

Cyber-physical systems and Internet of Things (IoT) form two different levels of the vertical digital integration. Integration of websites with IoT-connected devices has compelled creation of new web design and development strategies where websites are designed keeping in mind the permutations of smart devices. The design should be seamless across different devices and the website design company or web designer should be well informed and aware of the different considerations for design with IoT interactions. In this work, we expound the effectiveness of IoT integration in website design. To realize an IoT-powered IT ecosystem as an essential technology for improving customer experience, a strength–weakness–opportunity–threat analysis is done. Further, with an intent to apprehend the integration support that an existing GUI front end may provide to a smart device, an ANFIS model is proposed to determine the compatibility of an e-commerce website for integration with IoT devices. A dataset of 600 e-commerce websites from.com domain is used to train and test the learning model. Seven features (page loading speed, broken links, browser compatibility, resolution, total size, privacy and security, and interface and typography) which impact the compatibility of IoT integration in websites have been used. Evaluation criteria for assigning score to each feature has been identified. Finally, the compatibility score, the IoTScoresite which evaluates the websites’ integration capabilities and support to IoT devices is generated by adding all the feature scores. The preliminary results generated using the prediction model clearly determine the worthiness of website for IoT integration

Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding which of the internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the neural net with an oblique decision tree having sparse weight vectors at the decision nodes. Using the recently proposed Tree Alternating Optimization (TAO) algorithm, we are able to learn trees that are both highly accurate and interpretable. Such trees can faithfully mimic the part of the neural net they replaced, and hence they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. These insights and manipulations apply globally to the entire training and test set, not just at a local (single-instance) level. We demonstrate this robustly in the MNIST and ImageNet datasets with LeNet5 and VGG networks

Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees

THIS PAPER HAS BEEN RETRACTED The University of California, Merced campus, recently completed a formal investigation into allegations that content in this paper ("Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees," in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 5133– 5142) was not properly attributed to the novel work of Professor Carreira—Perpiñán, reflected in a previously published paper, "Alternating Optimization of Decision Trees, with Application to Learning Sparse Oblique Trees,” published in Advances in Neural Information Processing Systems, 2018, (vol. 31, pp. 1211–1221). The UC Merced investigation was conducted in consultation with NSF, which funded the research reflected in the original Advances in Neural Information Processing Systems paper, and in accordance with UC Merced policies and procedures on research misconduct. The allegations were substantiated by a preponderance of the evidence. The investigation committee found, in pertinent part, that “key novel ideas in the AAAI paper were taken from the NeurIPS paper without appropriate credit." The initial submission of the AAAI paper, upon which the acceptance decision was made, did not include a reference to the NeurIPS paper, and thus represented a clear case of plagiarism. A citation to the NeurIPS paper was added in the final revision, after the reviewers had already decided to accept it, making it impossible for the AAAI peer-review process to consider the contributions of the AAAI article in light of the earlier work reported in the NeurIPS paper. Furthermore, the citation to the NeurIPS paper in this manuscript does not properly credit the original contributions of the NeurIPS paper and how those ideas were reflected in the work reported in this paper. In response to a formal request made by the University of California's Chancellor's office, and after careful review by the AAAI Publications Committee, AAAI has decided to retract this paper. One of the conditions of submission of a paper for publication in AAAI conference proceedings is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Reuse of any data should be appropriately cited. As such this paper represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of this proceedings that this was not detected during the submission process