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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
Transfer learning for time series classification
Transfer learning for deep neural networks is the process of first training a
base network on a source dataset, and then transferring the learned features
(the network's weights) to a second network to be trained on a target dataset.
This idea has been shown to improve deep neural network's generalization
capabilities in many computer vision tasks such as image recognition and object
localization. Apart from these applications, deep Convolutional Neural Networks
(CNNs) have also recently gained popularity in the Time Series Classification
(TSC) community. However, unlike for image recognition problems, transfer
learning techniques have not yet been investigated thoroughly for the TSC task.
This is surprising as the accuracy of deep learning models for TSC could
potentially be improved if the model is fine-tuned from a pre-trained neural
network instead of training it from scratch. In this paper, we fill this gap by
investigating how to transfer deep CNNs for the TSC task. To evaluate the
potential of transfer learning, we performed extensive experiments using the
UCR archive which is the largest publicly available TSC benchmark containing 85
datasets. For each dataset in the archive, we pre-trained a model and then
fine-tuned it on the other datasets resulting in 7140 different deep neural
networks. These experiments revealed that transfer learning can improve or
degrade the model's predictions depending on the dataset used for transfer.
Therefore, in an effort to predict the best source dataset for a given target
dataset, we propose a new method relying on Dynamic Time Warping to measure
inter-datasets similarities. We describe how our method can guide the transfer
to choose the best source dataset leading to an improvement in accuracy on 71
out of 85 datasets.Comment: Accepted at IEEE International Conference on Big Data 201
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge
This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play
Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge
Pit platform. We briefly describe the scope and background of this competition
in the context of a more general project related to the development of an AI
engine for video games, called Grail. We also discuss the outcomes of this
challenge and demonstrate how predictive models for the assessment of player's
winning chances can be utilized in a construction of an intelligent agent for
playing Hearthstone. Finally, we show a few selected machine learning
approaches for modeling state and action values in Hearthstone. We provide
evaluation for a few promising solutions that may be used to create more
advanced types of agents, especially in conjunction with Monte Carlo Tree
Search algorithms.Comment: Federated Conference on Computer Science and Information Systems,
Prague (FedCSIS-2017) (Prague, Czech Republic
MineGAN: effective knowledge transfer from GANs to target domains with few images
One of the attractive characteristics of deep neural networks is their
ability to transfer knowledge obtained in one domain to other related domains.
As a result, high-quality networks can be trained in domains with relatively
little training data. This property has been extensively studied for
discriminative networks but has received significantly less attention for
generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained
GANs is a desirable objective. We propose a novel knowledge transfer method for
generative models based on mining the knowledge that is most beneficial to a
specific target domain, either from a single or multiple pretrained GANs. This
is done using a miner network that identifies which part of the generative
distribution of each pretrained GAN outputs samples closest to the target
domain. Mining effectively steers GAN sampling towards suitable regions of the
latent space, which facilitates the posterior finetuning and avoids pathologies
of other methods such as mode collapse and lack of flexibility. We perform
experiments on several complex datasets using various GAN architectures
(BigGAN, Progressive GAN) and show that the proposed method, called MineGAN,
effectively transfers knowledge to domains with few target images,
outperforming existing methods. In addition, MineGAN can successfully transfer
knowledge from multiple pretrained GANs. Our code is available at:
https://github.com/yaxingwang/MineGAN.Comment: CVPR202
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
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