{"title":"Reduction with Application to Pattern Recognition in Large Databases","authors":"I. Perfilieva, P. Hurtík","doi":"10.1109/SSCI.2018.8628802","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628802","url":null,"abstract":"Two distinguished properties of the F-transform: the best approximation in a local sense and the reduction in dimension imply the fact that the F-transform has many successful applications. In the first part, we propose another way of computing the F-transform components of a functional data. This way is based on the particular dimensionality reduction algorithm named Laplacian eigenmaps. In the second part, we strengthen the effect of F-transform-based dimensionality reduction by applying the PCA reduction method over the $F^{0}-$ or $F^{1}-$ transform results. We demonstrate the efficiency of the proposed combinations $F^{0}zT+PCA$ and $F^{1}zT+PCA$ on the problem of patter recognition in a large database. We compare both combinations with other relevant techniques (besides other, LENET-like CNN) and show that they outperform them from the computation time and success rate points of view.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Model Decomposition for Forward Model Approximation","authors":"Alexander Dockhorn, Tim Tippelt, R. Kruse","doi":"10.1109/SSCI.2018.8628624","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628624","url":null,"abstract":"In this paper we propose a model decomposition architecture, which advances on our previous attempts of learning an approximated forward model for unknown games [1]. The developed model architecture is based on design constraints of the General Video Game Artificial Intelligence Competition and the Video Game Definition Language. Our agent first builds up a database of interactions with the game environment for each distinct component of a game. We further train a decision tree model for each of those independent components. For predicting a future state we query each model individually and aggregate the result. The developed model ensemble does not just predict known states with a high accuracy, but also adapts very well to previously unseen levels or situations. Future work will show how well the increased accuracy helps in playing an unknown game using simulation-based search algorithms such as Monte Carlo Tree Search.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Sparse Bayesian Reinforcement Learning: A Framework for Interpreting What an Agent Has Learned","authors":"Indrajeet Mishra, Giang Dao, Minwoo Lee","doi":"10.1109/SSCI.2018.8628887","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628887","url":null,"abstract":"This paper presents a Visual Sparse Bayesian Reinforcement Learning (V-SBRL) framework for recording the images of the most important memories from the past experience. The key idea of this paper is to maintain an image snapshot storage to help understanding and analyzing the learned policy. In the extended framework of SBRL [1], the agent perceives the environment as the image state inputs, encodes the image into feature vectors, train SBRL module and stores the raw images. In this process, the snapshot storage keeps only the relevant memories which are important to make future decisions and discards the not-so-important memories. The stored snapshot images enable us to understand the agent’s learning process by visualizing them. They also provide explanation of exploited policy in different conditions. A navigation task with static obstacles is examined for snapshot analysis.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"525 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116706136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ensemble Incremental Random Vector Functional Link Network for Short-term Crude Oil Price Forecasting","authors":"Xueheng Qiu, P. N. Suganthan, G. Amaratunga","doi":"10.1109/SSCI.2018.8628724","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628724","url":null,"abstract":"In this paper, an ensemble incremental learning model composed of Empirical Mode Decomposition (EMD), Random Vector Functional Link network (RVFL) and Incremental RVFL is presented in this work. First of all, EMD is employed to decompose the historical crude oil price time series. Then each sub-signal is modeled by an RVFL model to generate the corresponding forecast IMF value. Finally, the prediction results of all IMFs are combined to formulate an aggregated output for crude oil price. By introducing incremental learning, along with EMD based ensemble methods into RVFL network, the forecasting performance can be significantly improved with respect to both efficiency and accuracy. The crude oil price datasets from West Texas Intermediate (WTI) and Brent oil are used to test the effectiveness of the proposed EMD-Incremental-RVFL method. Simulation results demonstrated attractiveness of the proposed method compared with seven benchmark methods including long short-term memory (LSTM) network, especially based on fast computation speed.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Sesma-Sara, C. Marco-Detchart, J. Lafuente, A. Roldán, R. Mesiar, H. Bustince
{"title":"Directions of directional, ordered directional and strengthened ordered directional increasingness of linear and ordered linear fusion operators","authors":"M. Sesma-Sara, C. Marco-Detchart, J. Lafuente, A. Roldán, R. Mesiar, H. Bustince","doi":"10.1109/SSCI.2018.8628679","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628679","url":null,"abstract":"In this work we discuss the forms of monotonicity that have been recently introduced to relax the monotonicity condition in the definition of aggregation functions. We focus on directional, ordered directional and strengthened ordered directional monotonicity, study their main properties and provide some results about their links and relations among them. We also present two families of functions, the so-called linear fusion functions and ordered linear fusion functions and we study the set of directions for which these types of functions are directionally, ordered directionally and strengthened ordered directionally increasing. In particular, OWA operators are an example of ordered linear fusion functions.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127236345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improved universum twin support vector machine","authors":"Bharat Richhariya, A. Sharma, M. Tanveer","doi":"10.1109/SSCI.2018.8628671","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628671","url":null,"abstract":"Universum based learning provides prior information about data in the optimization problem of support vector machine (SVM). Universum twin support vector machine (UTSVM) is a computationally efficient algorithm for classification problems. It solves a pair of quadratic programming problems (QPPs) to obtain the classifier. In order to include the structural risk minimization (SRM) principle in the formulation of UTSVM, we propose an improved universum twin support vector machine (IUTSVM). Our proposed IUTSVM implicitly makes the matrices non-singular in the optimization problem by adding a regularization term. Several numerical experiments are performed on benchmark real world datasets to verify the efficacy of our proposed IUTSVM. The experimental results justifies the better generalization performance of our proposed IUTSVM in comparison to existing algorithms.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127249638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hugo Manuel Proença, R. Klijn, Thomas Bäck, M. Leeuwen
{"title":"Identifying flight delay patterns using diverse subgroup discovery","authors":"Hugo Manuel Proença, R. Klijn, Thomas Bäck, M. Leeuwen","doi":"10.1109/SSCI.2018.8628933","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628933","url":null,"abstract":"Flight delay is a common hassle that affects around one fourth of flights and has been a major concern for airlines for decades. Therefore, an increasing amount of research was done on this topic in recent years. Notably, the fields of machine learning and data mining have proposed various solutions for the prediction of flight delays, typically some hours before departure. However, the most important decisions made by airlines that could benefit from such predictions, i.e., those on scheduled block time and crew schedules, are made between two to six months prior to departure. Consequently, late delay predictions are useless for these scheduling tasks.As accurately predicting delays for individual flights a long time in advance is practically infeasible, we instead propose to search for circumstances associated to large delays. For this we propose to use diverse Subgroup Discovery (SD), a data mining technique that allows to discover subsets of the data that 1) deviate from the overall data with regard to some target variable, and 2) can be described by a simple conjunctive query on the other variables. We apply diverse SD to historic flight data and mine subgroups of flights that, on average, have a large delay. We show that this approach gives subgroups that can be easily understood by experts, despite the fact that non-trivial relations between multiple variables can be discovered. We show that using diverse SD gives less redundant results than standard top-k SD and demonstrate that even in situations where inferring an accurate predictive model is infeasible, local deviations can be effectively captured and described by local patterns, potentially providing valuable insights for, e.g., airline scheduling problems.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124952977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid GA-PCA Feature Selection Approach for Inertial Human Activity Recognition","authors":"Ayman M. Abo El-Maaty, A. Wassal","doi":"10.1109/SSCI.2018.8628702","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628702","url":null,"abstract":"Genetic algorithms is used as a wrapper feature selection technique in many research studies. In this paper we investigate GA capabilities in selecting the best set of time-series features for human activity recognition application. We propose a hybrid GA-PCA approach, where GA is used to select a subset of N features from 561 features, then PCA is used to reduce the subset into M orthogonal features. Experimental results show the ability of GA to eliminate low performance features without affecting the classification accuracy.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Moiré Pattern Detection using Wavelet Decomposition and Convolutional Neural Network","authors":"E. Abraham","doi":"10.1109/SSCI.2018.8628746","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628746","url":null,"abstract":"Moiré patterns are interference patterns that are produced due to the overlap of the digital grids of the camera sensor resulting in a high-frequency noise in the image. This paper proposes a new method to detect Moiré patterns using wavelet decomposition and a multi-input deep Convolutional Neural Network (CNN), for images captured from a computer screen. Also, this paper proposes a method to use the normalized intensity values in the image, as weights for the frequency strength of Moiré pattern. The CNN model created with this approach is robust to high background frequencies other than those of Moiré patterns, as the model is trained using images captured considering diverse scenarios. We have tested this model in receipt scanning application, to detect the Moiré patterns produced in the images captured from a computer screen, and achieved an accuracy of 98.4%.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"82 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114044923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of Pulse Repetition Interval Modulations Using Neural Networks","authors":"H. P. K. Nguyen, H. Nguyen","doi":"10.1109/SSCI.2018.8628913","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628913","url":null,"abstract":"Repetition Intervals (PRI)-the distances between consecutive times of arrival of radar pulses-are important characteristics that help identify the emitting source. The recognition of various PRI modulation types under the assumption of missing and spurious pulses is a classical yet challenging problem. We introduce in this paper a novel learning-based method for the classification of 7 popular PRI modulations. In this classifier, a set of 6 features, extracted from the preprocessed PRI sequences, are fed into a simple feed-forward neural network. The proposed scheme, while computationally fast, outperforms existing methods by a significant margin on a variety of PRI parameters and under different levels of pulse miss-detections and false alarms.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114254100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}