2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第3页

Complex Decomposition of the Negative Distance Kernel 负距离核的复分解

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.151

Tim vor der Brück, Steffen Eger, Alexander Mehler

引用次数: 1

EEG-based Secondary Task Detection in a Multiple Objective Operational Environment 多目标作战环境下基于脑电图的辅助任务检测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.107

Joseph J. Giametta, B. Borghetti

引用次数: 3

Using Vector Quantization of Hough Transform for Circle Detection 基于霍夫变换的矢量量化圆检测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.94

Bing Zhou

引用次数: 9

Deep Neural Networks: A Case Study for Music Genre Classification 深度神经网络:音乐类型分类的案例研究

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.160

A. Rajanna, Kamelia Aryafar, A. Shokoufandeh, R. Ptucha

引用次数: 30

Statistical Scenarios for Demand Forecast of a High Voltage Feeder: A Comparative Study 高压馈线需求预测的统计方案:比较研究

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.34

R. Bayindir, M. Yesilbudak, U. Çetinkaya, H. Bulbul, F. Arslan

引用次数: 1

Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data? 数据采样的加入是否提高了非平衡生物信息学数据的增强算法的性能?

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.23

Alireza Fazelpour, T. Khoshgoftaar, D. Dittman, Amri Napolitano

{"title":"Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data?","authors":"Alireza Fazelpour, T. Khoshgoftaar, D. Dittman, Amri Napolitano","doi":"10.1109/ICMLA.2015.23","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.23","url":null,"abstract":"Bioinformatics datasets contain many challenging characteristics, such as class imbalance, which adversely impacts the performance of supervised classification models built on these datasets. Techniques such as ensemble learning and data sampling from the domain of data mining can be deployed to alleviate the problem and to improve the classification performance. In this study, we sought to seek whether inclusion of data sampling within the ensemble framework can further improve the performance of classification models. To this end, we performed an experimental study using two newly hybrid ensemble techniques, one integrates feature selection within the boosting process and the other incorporates random under-sampling followed by feature selection within the boosting framework, two learners, three forms of feature rankers, and four feature subset sizes on 15 highly imbalanced bioinformatics datasets. Our results and statistical analysis demonstrate that the difference between the two boosting methods is statistically insignificant. Therefore, as the inclusion of data sampling has no significant positive effect on the performance of ensemble classifiers, it is not required to achieve maximum classification performance. To our knowledge, this is the first empirical study that examined the effects of data sampling, random under-sampling, to enhance classification performance of boosting algorithm for highly imbalanced bioinformatics data.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126384076","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}

引用次数: 0

Predicting Energy Demand Peak Using M5 Model Trees 利用M5模型树预测能源需求峰值

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.164

Sara S. Abdelkader, Katarina Grolinger, Miriam A. M. Capretz

{"title":"Predicting Energy Demand Peak Using M5 Model Trees","authors":"Sara S. Abdelkader, Katarina Grolinger, Miriam A. M. Capretz","doi":"10.1109/ICMLA.2015.164","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.164","url":null,"abstract":"Predicting energy demand peak is a key factor for reducing energy demand and electricity bills for commercial customers. Features influencing energy demand are many and complex, such as occupant behaviours and temperature. Feature selection can decrease prediction model complexity without sacrificing performance. In this paper, features were selected based on their multiple linear regression correlation coefficients. This paper discusses the capabilities of M5 model trees in energy demand prediction for commercial buildings. M5 model trees are similar to regression trees, however they are more suitable for continuous prediction problems. The M5 model tree prediction was developed based on a selected feature set including sensor energy demand readings, day of the week, season, humidity, and weather conditions (sunny, rain, etc.). The performance of the M5 model tree was evaluated by comparing it to the support vector regression (SVR) and artificial neural networks (ANN) models. The M5 model tree outperformed the SVR and ANN models with a mean absolute error (MAE) of 8.94 compared to 10.02 and 12.04 for the SVR and ANN models respectively.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116056622","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}

引用次数: 11

Investigating Eating Behaviours Using Topic Models 使用主题模型调查饮食行为

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.50

Ruth White, W. Harwin, W. Holderbaum, Laura Johnson

引用次数: 3

Concept of 4th Dimension for Databases 数据库的第四维概念

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.186

E. Irmak, Ömer Kurtuldu

引用次数: 2

A Bayesian Classification Approach to Improving Performance for a Real-World Sales Forecasting Application 一种贝叶斯分类方法提高实际销售预测应用的性能

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.150

C. Gallagher, M. G. Madden, Brian D'Arcy

{"title":"A Bayesian Classification Approach to Improving Performance for a Real-World Sales Forecasting Application","authors":"C. Gallagher, M. G. Madden, Brian D'Arcy","doi":"10.1109/ICMLA.2015.150","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.150","url":null,"abstract":"Many businesses rely on forecasting techniques to detect whether sales opportunities are likely to be won or at risk of being lost. This enables the businesses to respond proactively. This paper describes a new method of sales forecasting that improves on an existing Qualitative Sales Predictor (QSP) in Hewlett-Packard (HP). QSP is based on a series of qualitative assessments that are made by sales personnel, the results of which are combined using weighted factors. In this research, we have developed an alternative method of forecasting sales opportunities, with three key differences: (1) the qualitative assessments are supplemented with quantitative data describing attributes of the opportunity, (2) we replace the weight factors with a Tree Augmented Naïve Bayes (TAN) classifier that can capture dependences between variables and produces a probabilistic output to which thresholds can be applied, (3) the TAN classifier is of course learned from historical data, whereas the existing QSP has fixed weights. Our approach has an accuracy of 90.6% in predicting whether sales will be won or lost, a substantial improvement on the existing approach's accuracy of 75.6% on the same unseen test data.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123815976","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}

引用次数: 5