María de Lourdes Martínez-Villaseñor, M. González-Mendoza
In highly dynamic environments it is not enough to model the user in order to provide proactive and personalized services. User features, preferences and needs change depending on different contextual aspects such as physical, social and computational conditions. Taking context into account in these environments implies coping with high openness and dynamicity of users and devices. Moreover, context modeling and context management is a complex task performed repeatedly in distributed environments, and users constantly share information about current activities, location, social events, goals, etc. In different applications. There is huge context information scattered over user's applications and devices that can be taken advantage of to provide more accurate adaptation and personalization. In this paper, we analyze the literature solutions with a focus on context information interoperability. We aim to identify basic requirements to perform the complex task of sharing and reusing context information between heterogeneous context providers and context consumers.
{"title":"Sharing and Reusing Context Information in Ubiquitous Computing Environments","authors":"María de Lourdes Martínez-Villaseñor, M. González-Mendoza","doi":"10.1109/MICAI.2014.41","DOIUrl":"https://doi.org/10.1109/MICAI.2014.41","url":null,"abstract":"In highly dynamic environments it is not enough to model the user in order to provide proactive and personalized services. User features, preferences and needs change depending on different contextual aspects such as physical, social and computational conditions. Taking context into account in these environments implies coping with high openness and dynamicity of users and devices. Moreover, context modeling and context management is a complex task performed repeatedly in distributed environments, and users constantly share information about current activities, location, social events, goals, etc. In different applications. There is huge context information scattered over user's applications and devices that can be taken advantage of to provide more accurate adaptation and personalization. In this paper, we analyze the literature solutions with a focus on context information interoperability. We aim to identify basic requirements to perform the complex task of sharing and reusing context information between heterogeneous context providers and context consumers.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435486","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}
Israel Tabarez Paz, N. Hernández-Gress, M. González-Mendoza, David González-Marrón
This manuscript is focused on scalability of Spiking Neural Network (SNN) for acceleration of its learning time. Simulation of SNN algorithm was implemented on GPUs devices Ge Force 9400M and Ge Force650 GTX in order to compare the learning time. Multiclass database are used for classification and the results are compared.
本文主要研究了尖峰神经网络(SNN)在加速其学习时间方面的可扩展性。在gpu设备Ge Force 9400M和Ge Force650 GTX上对SNN算法进行仿真，比较学习时间。采用多类数据库进行分类，并对分类结果进行比较。
{"title":"Scalability of Multiclass Simulation of Spiking Neural Networks on GPUs","authors":"Israel Tabarez Paz, N. Hernández-Gress, M. González-Mendoza, David González-Marrón","doi":"10.1109/MICAI.2014.21","DOIUrl":"https://doi.org/10.1109/MICAI.2014.21","url":null,"abstract":"This manuscript is focused on scalability of Spiking Neural Network (SNN) for acceleration of its learning time. Simulation of SNN algorithm was implemented on GPUs devices Ge Force 9400M and Ge Force650 GTX in order to compare the learning time. Multiclass database are used for classification and the results are compared.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123204680","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}
G. González-Campos, L. Torres-Treviño, E. Luévano-Hipólito, A. M. Cruz
Symbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the synthesis process of the oxides Bi2MoO6 and V2O5 in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them.
{"title":"Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-β","authors":"G. González-Campos, L. Torres-Treviño, E. Luévano-Hipólito, A. M. Cruz","doi":"10.1109/MICAI.2014.33","DOIUrl":"https://doi.org/10.1109/MICAI.2014.33","url":null,"abstract":"Symbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the synthesis process of the oxides Bi2MoO6 and V2O5 in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287385","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. Mohammadi, H. Parvin, N. Nematbakhsh, A. Heidarzadegan
Clustering the genes based on their expression patterns is one of the important subjects in analyzing microarray data. Discovering the genes co-expressed in particular conditions has been done by different clustering algorithms. In these methods, the similar genes are located in the same cluster. Thus, the closer the similar genes, the further the dissimilar ones will be. Each of the applied methods to discover gene clusters has had advantages and drawbacks. The proposed method, which is density-based hierarchical, is robust enough due to discovering clusters with different shapes and detecting noise. Moreover, its hierarchical characteristic illustrates a proper image of data distribution and their relationships. In this paper, the results obtained from executing the algorithm for 30 times show it has notable accuracy to capture clusters, in a way that it is 98% for extracting three-cluster gene networks and 70% for four-cluster ones.
{"title":"A Robust Density-Based Hierarchical Clustering Algorithm","authors":"M. Mohammadi, H. Parvin, N. Nematbakhsh, A. Heidarzadegan","doi":"10.1109/MICAI.2014.19","DOIUrl":"https://doi.org/10.1109/MICAI.2014.19","url":null,"abstract":"Clustering the genes based on their expression patterns is one of the important subjects in analyzing microarray data. Discovering the genes co-expressed in particular conditions has been done by different clustering algorithms. In these methods, the similar genes are located in the same cluster. Thus, the closer the similar genes, the further the dissimilar ones will be. Each of the applied methods to discover gene clusters has had advantages and drawbacks. The proposed method, which is density-based hierarchical, is robust enough due to discovering clusters with different shapes and detecting noise. Moreover, its hierarchical characteristic illustrates a proper image of data distribution and their relationships. In this paper, the results obtained from executing the algorithm for 30 times show it has notable accuracy to capture clusters, in a way that it is 98% for extracting three-cluster gene networks and 70% for four-cluster ones.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"63 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123311175","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}
Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Difference in activation patterns of schizophrenia patients and healthy subjects can be identified with the help of functional magnetic resonance imaging (fMRI). However, manual diagnosis using fMRI depends on subjective observation and may be erroneous. This has motivated the pattern recognition and machine learning research community to develop a reliable and efficient decision model for classification of schizophrenia patients and healthy subjects. However, high dimensionality and low availability of fMRI data leads to the curse-of-dimensionality problem which may degrade the performance of decision model. In the present research work, a combination of feature extraction and feature selection techniques is utilised to obtain a reduced set of relevant and non-redundant features for classification of schizophrenia patients and healthy subjects. Features are extracted from pre-processed fMRI data using a general linear model approach. Next Fisher's discriminant ratio, a univariate method, is employed for feature selection i.e. To determine a subset of discriminative features. Further, minimum Redundancy Maximum Relevance (mRMR) feature selection, a multivariate method, is employed to obtain a set of relevant and non-redundant features which are used for learning a decision model using support vector machine. Two balanced and well-age matched datasets of auditory oddball task derived from a publicly available multisite FBIRN dataset are used for experiments. First dataset consists of fMRI scans of 34 schizophrenia patients and 34 healthy subjects acquired through 1.5 Tesla scanners while second dataset consists of 25 schizophrenia patients and 25 healthy subjects acquired through 3 Tesla scanners. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches for classification of schizophrenia patients and healthy subjects using fMRI. Experimental results demonstrate that the proposed model outperforms the existing approaches in terms of sensitivity, specificity and classification accuracy. The proposed approach achieves classification accuracy of 88.2% and 78.0% for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be potential biomarkers for diagnosis of schizophrenia using fMRI.
{"title":"A Novel Approach for Classification of Schizophrenia Patients and Healthy Subjects Using Auditory Oddball Functional MRI","authors":"A. Juneja, Bharti, R. Agrawal","doi":"10.1109/MICAI.2014.17","DOIUrl":"https://doi.org/10.1109/MICAI.2014.17","url":null,"abstract":"Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Difference in activation patterns of schizophrenia patients and healthy subjects can be identified with the help of functional magnetic resonance imaging (fMRI). However, manual diagnosis using fMRI depends on subjective observation and may be erroneous. This has motivated the pattern recognition and machine learning research community to develop a reliable and efficient decision model for classification of schizophrenia patients and healthy subjects. However, high dimensionality and low availability of fMRI data leads to the curse-of-dimensionality problem which may degrade the performance of decision model. In the present research work, a combination of feature extraction and feature selection techniques is utilised to obtain a reduced set of relevant and non-redundant features for classification of schizophrenia patients and healthy subjects. Features are extracted from pre-processed fMRI data using a general linear model approach. Next Fisher's discriminant ratio, a univariate method, is employed for feature selection i.e. To determine a subset of discriminative features. Further, minimum Redundancy Maximum Relevance (mRMR) feature selection, a multivariate method, is employed to obtain a set of relevant and non-redundant features which are used for learning a decision model using support vector machine. Two balanced and well-age matched datasets of auditory oddball task derived from a publicly available multisite FBIRN dataset are used for experiments. First dataset consists of fMRI scans of 34 schizophrenia patients and 34 healthy subjects acquired through 1.5 Tesla scanners while second dataset consists of 25 schizophrenia patients and 25 healthy subjects acquired through 3 Tesla scanners. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches for classification of schizophrenia patients and healthy subjects using fMRI. Experimental results demonstrate that the proposed model outperforms the existing approaches in terms of sensitivity, specificity and classification accuracy. The proposed approach achieves classification accuracy of 88.2% and 78.0% for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be potential biomarkers for diagnosis of schizophrenia using fMRI.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129567567","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}
D. Lande, A. Snarskii, E. Yagunova, Ekaterina V. Pronoza, S. Volskaya
This paper describes the construction methodology of a network of natural terms hierarchy based on the analysis of a homogeneous or heterogeneous text corpus. It also presents a criterion for the evaluation of paper relevance to a particular scientific conference. The proposed method is illustrated by the examples from the heterogeneous corpus of the STIDS 2013 conference proceedings.
{"title":"Network of Natural Terms Hierarchy as a Lightweight Ontology","authors":"D. Lande, A. Snarskii, E. Yagunova, Ekaterina V. Pronoza, S. Volskaya","doi":"10.1109/MICAI.2014.9","DOIUrl":"https://doi.org/10.1109/MICAI.2014.9","url":null,"abstract":"This paper describes the construction methodology of a network of natural terms hierarchy based on the analysis of a homogeneous or heterogeneous text corpus. It also presents a criterion for the evaluation of paper relevance to a particular scientific conference. The proposed method is illustrated by the examples from the heterogeneous corpus of the STIDS 2013 conference proceedings.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122214861","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}
Mental state examination (MSE) involves assessing the overall severity of illness and also differentiating likely diagnoses. When it is performed on patients serially during the period of their illness, the consecutive estimates can serve as an important way to track their recovery. However, the traditional approach to mental state examination that uses clinician's subjective judgement and results in subjective estimates can be unreliable and prone to inconsistencies. Using the approach introduced in this paper, a case is represented as a vector of thirty five different clinical features, which are rated using a numerical scale according to the severity of each clinical feature. The vector length is used as a measure of the overall severity of the illness. The case base consists of one standard case for each of 6 diagnostic categories. Each standard case represents a typical case for its diagnostic category, with each clinical feature rated according to the maximum level of severity that can be expected for that category. Evaluation of a given clinical case, with clinical features as rated by a clinician with regard to the likely diagnoses involves measuring the similarity of the resulting case vector with the standard vectors in the case base. Whilst cosine similarity and Euclidean distance are alternative measures of similarity, a more clinically intuitive and accurate measure based on orthogonal vector projection is proposed. The orthogonal vector projection approach to case based assessment was evaluated using thirty different test cases representing six different common diagnostic categories. For each of the test cases similarity measures obtained using orthogonal vector projection were compared with measures obtained using cosine similarity and Euclidean distance. The results indicated that the orthogonal vector projection approach was able to differentiate both the diagnosis and severity of illness more accurately than the other two similarity measures. The proposed approach has the potential to be used as a standardised clinical tool for both establishing the diagnosis and severity of illness, and also measuring the recovery from illness. In particular, the estimates of recovery obtained from this approach can serve as an important index in healthcare economics.
{"title":"A Case-Based Reasoning Approach to Mental State Examination Using a Similarity Measure Based on Orthogonal Vector Projection","authors":"Irosh Fernando, F. Henskens","doi":"10.1109/MICAI.2014.43","DOIUrl":"https://doi.org/10.1109/MICAI.2014.43","url":null,"abstract":"Mental state examination (MSE) involves assessing the overall severity of illness and also differentiating likely diagnoses. When it is performed on patients serially during the period of their illness, the consecutive estimates can serve as an important way to track their recovery. However, the traditional approach to mental state examination that uses clinician's subjective judgement and results in subjective estimates can be unreliable and prone to inconsistencies. Using the approach introduced in this paper, a case is represented as a vector of thirty five different clinical features, which are rated using a numerical scale according to the severity of each clinical feature. The vector length is used as a measure of the overall severity of the illness. The case base consists of one standard case for each of 6 diagnostic categories. Each standard case represents a typical case for its diagnostic category, with each clinical feature rated according to the maximum level of severity that can be expected for that category. Evaluation of a given clinical case, with clinical features as rated by a clinician with regard to the likely diagnoses involves measuring the similarity of the resulting case vector with the standard vectors in the case base. Whilst cosine similarity and Euclidean distance are alternative measures of similarity, a more clinically intuitive and accurate measure based on orthogonal vector projection is proposed. The orthogonal vector projection approach to case based assessment was evaluated using thirty different test cases representing six different common diagnostic categories. For each of the test cases similarity measures obtained using orthogonal vector projection were compared with measures obtained using cosine similarity and Euclidean distance. The results indicated that the orthogonal vector projection approach was able to differentiate both the diagnosis and severity of illness more accurately than the other two similarity measures. The proposed approach has the potential to be used as a standardised clinical tool for both establishing the diagnosis and severity of illness, and also measuring the recovery from illness. In particular, the estimates of recovery obtained from this approach can serve as an important index in healthcare economics.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124636137","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}
This paper develops a novel sensorless vector control of induction motor (IM) drive robust against rotor resistance variation. The rotor resistance and speed are identified using extended Kalman filter (EKF). Then, we introduce a new fuzzy logic (FL) speed controller based on self learning by minimizing cost function. This approach is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. Indeed, the learning mechanism addresses the consequences of corrector rules, which are changed according to the comparison between the actual motor speed and an output signal or a desired trajectory. The FL associative memory is built to meet the criteria imposed in problems either control or pursuit. Inter alia, the consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady state performance. The robustness of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of IM drive.
{"title":"Extended Kalman Filter Based Learning Fuzzy for Parameters Adaptation of Induction Motor Drive","authors":"Moulay Rachid Douiri","doi":"10.1109/MICAI.2014.29","DOIUrl":"https://doi.org/10.1109/MICAI.2014.29","url":null,"abstract":"This paper develops a novel sensorless vector control of induction motor (IM) drive robust against rotor resistance variation. The rotor resistance and speed are identified using extended Kalman filter (EKF). Then, we introduce a new fuzzy logic (FL) speed controller based on self learning by minimizing cost function. This approach is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. Indeed, the learning mechanism addresses the consequences of corrector rules, which are changed according to the comparison between the actual motor speed and an output signal or a desired trajectory. The FL associative memory is built to meet the criteria imposed in problems either control or pursuit. Inter alia, the consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady state performance. The robustness of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of IM drive.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674309","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. Peña-Cabrera, M. J.Antonio-Gomez, R. Osorio, H. Gomez, V. Lomas, I. López-Juárez
An Omni directional mobile platform control using the intelligence technique of Fuzzy Logic is showed in the article, the control allows a practical and reliable driving control of 4 Omni directional wheels, implemented in FPGA allowing to have an independent and autonomous single chip system out of a central computer dependence in order to be used with different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone based on a smartphone OS Android as the handset control device. Driving movement for the mobile platform is limited for 8 directions, a Fuzzy Logic module controls the travelling of the platform with independent movement for each wheel, physical feedback is implemented by using electronic decoders.
{"title":"Fuzzy Logic for Omnidirectional Mobile Platform Control Based in FPGA and Bluetooth Communication","authors":"M. Peña-Cabrera, M. J.Antonio-Gomez, R. Osorio, H. Gomez, V. Lomas, I. López-Juárez","doi":"10.1109/MICAI.2014.27","DOIUrl":"https://doi.org/10.1109/MICAI.2014.27","url":null,"abstract":"An Omni directional mobile platform control using the intelligence technique of Fuzzy Logic is showed in the article, the control allows a practical and reliable driving control of 4 Omni directional wheels, implemented in FPGA allowing to have an independent and autonomous single chip system out of a central computer dependence in order to be used with different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone based on a smartphone OS Android as the handset control device. Driving movement for the mobile platform is limited for 8 directions, a Fuzzy Logic module controls the travelling of the platform with independent movement for each wheel, physical feedback is implemented by using electronic decoders.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128597938","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}
Jorge Melgoza-Gutierrez, A. Guerra-Hernández, N. Cruz-Ramírez
This paper presents a collaborative learning protocol dealing with vertical partitions in training data, i.e., The attributes of the instances are distributed in different data sources. The protocol has been modeled and implemented following the Agents and Artifacts paradigm. The artifacts provide Weka based learning tools to induce and evaluate Decision Trees (a modified version of J48), While the agents manage the workflow of the learning process, using such tools. The proposed protocol, and slightly faster variation, are tested with some known training sets of the UCI repository, comparing the obtained accuracy against that obtained in a centralized scenario. Our collaborative learning protocol achieves equivalent accuracy to that obtained with centralized data, while preserving privacy.
{"title":"Collaborative Data Mining on a BDI Multi-agent System over Vertically Partitioned Data","authors":"Jorge Melgoza-Gutierrez, A. Guerra-Hernández, N. Cruz-Ramírez","doi":"10.1109/MICAI.2014.39","DOIUrl":"https://doi.org/10.1109/MICAI.2014.39","url":null,"abstract":"This paper presents a collaborative learning protocol dealing with vertical partitions in training data, i.e., The attributes of the instances are distributed in different data sources. The protocol has been modeled and implemented following the Agents and Artifacts paradigm. The artifacts provide Weka based learning tools to induce and evaluate Decision Trees (a modified version of J48), While the agents manage the workflow of the learning process, using such tools. The proposed protocol, and slightly faster variation, are tested with some known training sets of the UCI repository, comparing the obtained accuracy against that obtained in a centralized scenario. Our collaborative learning protocol achieves equivalent accuracy to that obtained with centralized data, while preserving privacy.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902332","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}
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