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An Acceleration Method for computing Dominace Classes in Ordered Information System

An Acceleration Method for Computing Dominace Classes in Ord...

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international conference on machine learning and Cybernetics (icmlc)

作者： Yan Li Jing Zhang Qiang He Siyuan Liu Lujing Huo School of Applied Mathematics Beijing Normal University Zhuhai Zhuhai China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei China School of Science Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781728128177

In rough set theory, two crisp sets (i.e., the lower and upper approximates of a target concept) is used to describe uncertainties in given information systems. However, the traditional rough set models are built based on equivalence relations which do not consider the preference relationship of attribute values. Dominance relation-based rough set approach effectively solve this problem which uses dominance relations to substitute equivalence relations to deal with ordered data. In this kind of approach, the computing of dominance class is a necessary step to attribute reduction which is very time-consuming. In order to reduce the computational cost in calculating dominance classes, this paper presents a method to compute dominance classes by gradually reducing the search space in the domain. The corresponding algorithm is proposed. In each step of the algorithm, the inferior classes of the objects in a given information system are removed in the universe with the increase of the attributes. Experiments using six UCI data show that the proposed method improves the efficiency of computing dominance classes with the increasing of attributes and objects.

关键词： Rough set Dominance relation Dominance class Rough sets Computer based information systems Equivalence relation dominance ordered data acceleration information Classes

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House Prices Prediction with machine learning Algorithms 2018

House Prices Prediction with Machine Learning Algorithms

引用

10th international conference on machine learning and computing (icmlc)

作者： Fan, Chenchen Cui, Zechen Zhong, Xiaofeng Tsinghua Univ Elect Engn Dept ShuangQing Rd 30 Beijing Peoples R China Beijing Dongzhimen Middle Sch ShuangQing Rd 30 Beijing Peoples R China

ISBN: (纸本)9781450363532

Based on the data set compiled by D. D. Cock and the competition run by ***, we propose a house prices prediction algorithm in Ames, lowa by deliberating on data processing, feature engineering and combination forecasting. Our prediction ranks the 35th of the total 2221 results on the public leaderboard of *** and the RMSE of predicted results after taking logarithm from all the test data is 0.12019, which shows good performance and small of over-fitting.

关键词： House Prices Prediction machine learning Algorithms Combination Forecasting Kaggle Competition

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Distributed Broad learning System 2020

Distributed Broad Learning System

引用

12th international conference on machine learning and computing (icmlc)

作者： Zhai, Yifan Liu, Ying Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9781450376426

Recently, broad learning system (BLS) has been proposed and widely applied to the fields of machine learning and time series analysis. Compared with the well-known deep learning models, BLS does not need the deep architecture, and the learning process is time efficient. However, the existing BLS belongs to centralized processing, which is not applicable to the cases when data are distributed over multiple nodes. To solve this problem, in this paper, we propose a consensus-based distributed implementation of BLS (dBLS), in which each node cooperates with its one-hop neighbors to train the weights of the dBLS. Besides, a distributed extreme learning machine auto-encoder (dELM-AE) is also developed to refine the features extracted from the input data. Some simulations are performed and results show that the proposed dBLS is effective in solving distributed classification problems.

关键词： Broad learning system distributed processing consensus extreme learning machine auto-encoder

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Intersected EMG Heatmaps and Deep learning Based Gesture Recognition 2020

Intersected EMG Heatmaps and Deep Learning Based Gesture Rec...

引用

12th international conference on machine learning and computing (icmlc)

作者： Ke, Weijie Xing, Yannan Di Caterina, Gaetano Petropoulakis, Lykourgos Soraghan, John Univ Strathclyde CeSIP Royal Coll Bldg Glasgow Lanark Scotland

ISBN: (纸本)9781450376426

Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.

关键词： Convolutional Neural Network Gesture Recognition EMG Signal Processing

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Source Model Selection as a Meta-learning Technique to learn Novel Concepts 2020

Source Model Selection as a Meta-learning Technique to learn...

引用

12th international conference on machine learning and computing (icmlc)

作者： Kadam, Suvarna Vaidya, Vinay G. SPPU Dept Technol Pune Maharashtra India

ISBN: (纸本)9781450376426

Visual concept classification from a closed set has been successfully solved with supervised deep learning. However, learning a novel visual concept is challenging for deep models, especially when the concept is rare with only a few samples. Deep Transfer learning addresses this lack of training samples by using a model that is pre-trained on several visual concepts, as a feature extractor, to improve the performance on target concept. The effectiveness of such transfer depends on selection of relevant pretrained model. If we have multiple pretrained models, trained on different genre of visual concepts, we intuitively choose a model trained on the source visual concepts that are similar to target concept. In this paper, we attempt to quantify this human intuition that is crucial in human meta-learning for retrieving relevant knowledge. We propose an approach to quantitatively measure the suitability of source model to learn a novel and rare target concept, to automate the model selection which in effect achieves meta-learning.

关键词： Transfer learning Model Selection Visual Concept learning Generative machine learning

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Deep CNN with LM learning based myocardial ischemia detection in cardiac magnetic resonance images 41

Deep CNN with LM learning based myocardial ischemia detectio...

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41st Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Muthulakshmi, M. Kavitha, G. Anna Univ Dept Elect Engn MIT Campus Chennai 600044 Tamil Nadu India

ISBN: (纸本)9781538613115

Cardiovascular disease (CVD) is a chronic dysfunction caused by deterioration in cardiac physiology. It results in about 31% of mortality worldwide. Among CVDs, myocardial ischemia (MI) leads to restriction in blood supply to heart tissues. There is a need to develop an effective computer aided detection (CAD) system to reduce the fatality. In this work, an attempt is made to perform mass screening of myocardial ischemic subjects and left ventricle (LV) volume estimation from cardiac magnetic resonance (CMR) images using deep convolutional neural network (CNN) with Levenberg-Marquardt (LM) learning. LV volume measurement is an important predictor of myocardial ischemia. The CMR samples used in this analysis are obtained from Medical Image computing and Computer Assisted Intervention (MICCAI) 2009 database. The results of the proposed model are compared with deep CNN based on gradient descent (GD) learning algorithm. The results show that deep CNN architecture with LM learning classifies ischemic subjects with high accuracy (86.39%) and sensitivity (90%). The LM learning based method gives an AUC of 0.93. The estimated LV volumes obtained from the trained network gives high correlation with the ground truth. Thus the results support that proposed framework of deep CNN architecture with LM learning can be used as an effective CAD system for diagnosis of cardiovascular disorders.

关键词： Myocardium Training Volume measurement machine learning algorithms Convolutional neural networks Deep learning Heart

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A Novel Nonlinear Discriminant Classifier Trained by an Evolutionary Algorithm 2018

A Novel Nonlinear Discriminant Classifier Trained by an Evol...

引用

10th international conference on machine learning and computing (icmlc)

作者： Ursani, Ziauddin Corne, David W. Heriot Watt Univ Edinburgh Midlothian Scotland Route Monkey Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9781450363532

In recent work, we developed a classifier inspired by reliability engineering, in which the boundaries between classes was constrained based on parameterized formulae involving single feature probabilities. This produced competitive results on the 'Iris Flower' dataset, however it was not suitable for learning tasks with highly nonlinear class boundaries (where class membership correlates very poorly with any individual feature value). The 'Balance Scale' dataset is an example of the latter type of dataset, where individual features can only influence collectively on the decision of class membership. Keeping this in mind, in this paper we describe a new nonlinear discriminant classifier, in which an evolutionary algorithm learns a probabilistic model based on a constrained, parameterized combination of all feature values. We test this method on both the 'Iris Flower' and 'Balance Scale' datasets. The results are highly competitive.

关键词： machine learning Supervised learning Classification

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Distributed Semi-supervised Multi-label Classification with Quantized Communication 2020

Distributed Semi-supervised Multi-label Classification with ...

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12th international conference on machine learning and computing (icmlc)

作者： Xu, Zhen Zhai, Yifan Liu, Ying Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781450376426

In this paper, the problem of distributed multi-label classification (MLC) with a small portion of labeled data is considered, and a quantized distributed semi-supervised multi-label learning (QdS(2)ML(2)) algorithm is proposed. In the proposed algorithm, to utilize the information of both labeled and unlabeled data, information theoretic measures modeled by the kernel logistic regression function are used to design the loss function. Besides, to exploit the high-order label correlation over a network, a common low-dimensional subspace shared by multiple labels is learned in a distributed manner using quantized communication. Simulations on two real datasets are performed and results show that the proposed algorithm is very effective in solving MLC.

关键词： Semi-supervised learning multi-label classification information theoretic learning distributed processing quantized communication

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Applying machine learning to a Conventional Data Processing Task-A Quantitative Evaluation 2020

Applying Machine Learning to a Conventional Data Processing ...

引用

12th international conference on machine learning and computing (icmlc)

作者： Pree, Wolfgang Hoerbinger, Felix Univ Salzburg Dept Comp Sci Jakob Haringer Str 2 A-5020 Salzburg Austria

ISBN: (纸本)9781450376426

Though machine learning (ML) can be applied to a wide spectrum of applications, it has been hardly used and evaluated in the context of conventional data processing tasks. Such conventional data processing tasks are characterized by a set of calculations that follow strict rules, such as in accounting or banking applications. This paper quantitatively evaluates how software which is automatically generated by ML methods and tools compares to software programmed by hand. The assessment of poker hands according to Texas Hold'em rules is a representative example for conventional data processing tasks, because of the various exceptions how to assess and compare hands. For some hand values, the rank (two, three,... king, ace) of the cards is relevant and the suit (club, diamond, heart, spade) irrelevant, and vice versa. This paper shows how an accuracy of 100% can be achieved for assessing poker hands according to Texas Hold'em rules, with a small set of labeled training data compared to the number of possible hands. We also evaluate quantitatively the effect of the labeling quality on accuracy.

关键词： machine learning supervised learning neural networks convolutional neural networks feed-forward neural networks data labeling labeling quality robustness

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A Comparative Study for Classification on Different Domain 2018

A Comparative Study for Classification on Different Domain

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10th international conference on machine learning and computing (icmlc)

作者： Sagala, Noviyanti Tri Maretta Wang, Jenq-Haur Krida Wacana Christian Univ Dept Informat Syst Jakarta Indonesia Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781450363532

There is no individual classification technique has been shown to deal with all kinds of classification problems. The objective is to select the technique which more possibly reaches the best performance for any domain of data set. We focus on classifying datasets in different domains and properties such as numerical, categorical, and textual. We deal with one versus all strategy to handle multi-class problems. In the experiment, we compared the performance of 4 classification techniques namely Boosted C5.0, KNN, Naive Bayes, and SVM on 10-fold cross-validation on different number of features. For numerical data set (low and high dimensional data set), the performance of KNN was better than other classification methods. For a categorical and textual data set, Naive Bayes and SVM were outperformed, respectively.

关键词： Classification boosted C5.0 Naive Bayes k-nearest neighbor relief support vector machine machine learning

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