Lexical abstraction hierarchies can be leveraged to provide semantic information that characterizes features of text corpora as a whole. This information may be used to determine the classification utility of the dimensions that describe a dataset. This paper presents a new method for preparing a dataset for probabilistic classification by determining, a priori, the utility of a very small subset of taxonomically-related dimensions via a Discriminative Multinomial Naive Bayes process. We show that this method yields significant improvements over both Discriminative Multinomial Naive Bayes and Bayesian network classifiers alone.
{"title":"Taxonomic Dimensionality Reduction in Bayesian Text Classification","authors":"Richard A. McAllister, John W. Sheppard","doi":"10.1109/ICMLA.2012.93","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.93","url":null,"abstract":"Lexical abstraction hierarchies can be leveraged to provide semantic information that characterizes features of text corpora as a whole. This information may be used to determine the classification utility of the dimensions that describe a dataset. This paper presents a new method for preparing a dataset for probabilistic classification by determining, a priori, the utility of a very small subset of taxonomically-related dimensions via a Discriminative Multinomial Naive Bayes process. We show that this method yields significant improvements over both Discriminative Multinomial Naive Bayes and Bayesian network classifiers alone.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478448","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}
Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world avatar authentication and inter-reality-based scenarios of tracking a person between real and virtual worlds are reported. In the FERET-to-Avatar face dataset, where an avatar face was generated from every photo in the FERET database, a COTS FR algorithm achieved a near perfect 99.58% accuracy on 725 subjects. On a dataset of avatars from Second Life, the proposed avatar-to-avatar matching algorithm (which uses a fusion of local structural and appearance descriptors) achieved average true accept rates of (i) 96.33% using manual eye detection, and (ii) 86.5% in a fully automated mode at a false accept rate of 1.0%. A combination of the proposed face matcher and a state-of-the art commercial matcher (FaceVACS) resulted in further improvement on the inter-reality-based scenario.
{"title":"Face Recognition in the Virtual World: Recognizing Avatar Faces","authors":"Roman V. Yampolskiy, Brendan Klare, Anil K. Jain","doi":"10.1109/ICMLA.2012.16","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.16","url":null,"abstract":"Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world avatar authentication and inter-reality-based scenarios of tracking a person between real and virtual worlds are reported. In the FERET-to-Avatar face dataset, where an avatar face was generated from every photo in the FERET database, a COTS FR algorithm achieved a near perfect 99.58% accuracy on 725 subjects. On a dataset of avatars from Second Life, the proposed avatar-to-avatar matching algorithm (which uses a fusion of local structural and appearance descriptors) achieved average true accept rates of (i) 96.33% using manual eye detection, and (ii) 86.5% in a fully automated mode at a false accept rate of 1.0%. A combination of the proposed face matcher and a state-of-the art commercial matcher (FaceVACS) resulted in further improvement on the inter-reality-based scenario.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115485413","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 proposes a method which is suitable for the estimation of the probability of occurrence of a syndrome, as a function of the geographical coordinates of the individuals under risk. The data describing the location of syndrome cases over the population suffers a moving-average filtering, and the resulting values are fitted by an RBF network performing a regression. Some contour curves of the RBF network are then employed in order to establish the boundaries between four kinds of regions: regions of high-incidence, regions of medium incidence, regions of slightly-abnormal incidence, and regions of normal prevalence. In each region, the risk is estimated with three indicators: a nominal risk, an upper bound risk and a lower bound risk. Those indicators are obtained by adjusting the probability employed for the Monte Carlo simulation of syndrome scenarios over the population. The nominal risk is the probability which produces Monte Carlo simulations for which the empirical number of syndrome cases corresponds to the median. The upper bound and the lower bound risks are the probabilities which produce Monte Carlo simulations for which the empirical values of syndrome cases correspond respectively to the 25% percentile and the 75% percentile. The proposed method constitutes an advance in relation to the currently known techniques of spatial cluster detection, which are dedicated to finding clusters of abnormal occurrence of a syndrome, without quantifying the probability associated to such an abnormality, and without performing a stratification of different sub-regions with different associated risks. The proposed method was applied on data which were studied formerly in a paper that was intended to find a cluster of dengue fever. The result determined here is compatible with the cluster that was found in that reference.
{"title":"Risk Estimation in Spatial Disease Clusters: An RBF Network Approach","authors":"Fernanda C. Takahashi, Ricardo H. C. Takahashi","doi":"10.1109/ICMLA.2012.233","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.233","url":null,"abstract":"This paper proposes a method which is suitable for the estimation of the probability of occurrence of a syndrome, as a function of the geographical coordinates of the individuals under risk. The data describing the location of syndrome cases over the population suffers a moving-average filtering, and the resulting values are fitted by an RBF network performing a regression. Some contour curves of the RBF network are then employed in order to establish the boundaries between four kinds of regions: regions of high-incidence, regions of medium incidence, regions of slightly-abnormal incidence, and regions of normal prevalence. In each region, the risk is estimated with three indicators: a nominal risk, an upper bound risk and a lower bound risk. Those indicators are obtained by adjusting the probability employed for the Monte Carlo simulation of syndrome scenarios over the population. The nominal risk is the probability which produces Monte Carlo simulations for which the empirical number of syndrome cases corresponds to the median. The upper bound and the lower bound risks are the probabilities which produce Monte Carlo simulations for which the empirical values of syndrome cases correspond respectively to the 25% percentile and the 75% percentile. The proposed method constitutes an advance in relation to the currently known techniques of spatial cluster detection, which are dedicated to finding clusters of abnormal occurrence of a syndrome, without quantifying the probability associated to such an abnormality, and without performing a stratification of different sub-regions with different associated risks. The proposed method was applied on data which were studied formerly in a paper that was intended to find a cluster of dengue fever. The result determined here is compatible with the cluster that was found in that reference.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027312","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}
R. Passonneau, Ashish Tomar, Somnath Sarkar, Haimonti Dutta, Axinia Radeva
We assess the impact of an inspection repair program administered to the secondary electrical grid in New York City. The question of interest is whether repairs reduce the incidence of future events that cause service disruptions ranging from minor to serious ones. A key challenge in defining treatment and control groups in the absence of a randomized experiment involved an inherent bias in selection of electrical structures to be inspected in a given year. To compensate for the bias, we construct separate models for each year of the propensity for a structure to have an inspection repair. The propensity models account for differences across years in the structures that get inspected. To model the treatment outcome, we use a statistical approach based on the additive effects of many weak learners. Our results indicate that inspection repairs are more beneficial earlier in the five-year inspection cycle, which accords with the inherent bias to inspect structures in earlier years that are known to have problems.
{"title":"Multivariate Assessment of a Repair Program for a New York City Electrical Grid","authors":"R. Passonneau, Ashish Tomar, Somnath Sarkar, Haimonti Dutta, Axinia Radeva","doi":"10.1109/ICMLA.2012.208","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.208","url":null,"abstract":"We assess the impact of an inspection repair program administered to the secondary electrical grid in New York City. The question of interest is whether repairs reduce the incidence of future events that cause service disruptions ranging from minor to serious ones. A key challenge in defining treatment and control groups in the absence of a randomized experiment involved an inherent bias in selection of electrical structures to be inspected in a given year. To compensate for the bias, we construct separate models for each year of the propensity for a structure to have an inspection repair. The propensity models account for differences across years in the structures that get inspected. To model the treatment outcome, we use a statistical approach based on the additive effects of many weak learners. Our results indicate that inspection repairs are more beneficial earlier in the five-year inspection cycle, which accords with the inherent bias to inspect structures in earlier years that are known to have problems.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125457799","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}
A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature identification, and disaggregation using hidden Markov modeling (HMM), and residual analysis. The key contributions are (a) a novel 'segmented' application of the Viterbi algorithm for sequence decoding with the HMM, (b) details of establishing observation and state transition probabilities for the HMM, and (c) procedures for careful handling of low power signatures. Results show that the method is effective for magnitude-based disaggregation, and provide insights for a more complete solution.
{"title":"Unsupervised Disaggregation for Non-intrusive Load Monitoring","authors":"S. Pattem","doi":"10.1109/ICMLA.2012.249","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.249","url":null,"abstract":"A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature identification, and disaggregation using hidden Markov modeling (HMM), and residual analysis. The key contributions are (a) a novel 'segmented' application of the Viterbi algorithm for sequence decoding with the HMM, (b) details of establishing observation and state transition probabilities for the HMM, and (c) procedures for careful handling of low power signatures. Results show that the method is effective for magnitude-based disaggregation, and provide insights for a more complete solution.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"68 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120844123","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}
We propose a feature level fusion that is based on mapping the original low-level audio features to histogram descriptors. Our mapping is based on possibilistic membership functions and has two main components. The first one consists of clustering each set of features and identifying a set of representative prototypes. The second component uses the learned prototypes within membership functions to transform the original features into histograms. The mapping transforms features of different dimensions to histograms of fixed dimensions. This makes the fusion of multiple features less biased by the dimensionality and distributions of the different features. Using a standard collection of songs, we show that the transformed features provide higher classification accuracy than the original features. We also show that mapping simple low-level features and using a K-NN classifier provides results comparable to the state-of-the art.
{"title":"Feature Mapping and Fusion for Music Genre Classification","authors":"H. Balti, H. Frigui","doi":"10.1109/ICMLA.2012.59","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.59","url":null,"abstract":"We propose a feature level fusion that is based on mapping the original low-level audio features to histogram descriptors. Our mapping is based on possibilistic membership functions and has two main components. The first one consists of clustering each set of features and identifying a set of representative prototypes. The second component uses the learned prototypes within membership functions to transform the original features into histograms. The mapping transforms features of different dimensions to histograms of fixed dimensions. This makes the fusion of multiple features less biased by the dimensionality and distributions of the different features. Using a standard collection of songs, we show that the transformed features provide higher classification accuracy than the original features. We also show that mapping simple low-level features and using a K-NN classifier provides results comparable to the state-of-the art.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123929704","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}
In this paper, we present a prognosis architecture that allows the computation of the Remaining Useful Life (RUL) of a failing process. A process subject to an incipient fault experiments slowly developing degradation. Sensor measurements and Condition Monitoring (CM) data extracted from the system allow to follow up the process drift. The prognosis architecture we propose makes use of a dynamical clustering algorithm to model the data in a feature space. This algorithm uses a sliding window scheme on which the model is iteratively updated. Metrics applied on the parameters of this model are used to compute a drift severity indicator, which is also an indicator of the health of the system. The architecture for prognosis is applied on a benchmark of wind turbine. The used benchmark has been constructed to serve as a realistic wind turbine model. It was used in the context of a global scale fault diagnosis and fault tolerant control competition. The benchmark also proposed a drifting fault scenario that we used to test our approach.
{"title":"Prognosis Based on Handling Drifts in Dynamical Environments: Application to a Wind Turbine Benchmark","authors":"Antoine Chammas, E. Duviella, S. Lecoeuche","doi":"10.1109/ICMLA.2012.131","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.131","url":null,"abstract":"In this paper, we present a prognosis architecture that allows the computation of the Remaining Useful Life (RUL) of a failing process. A process subject to an incipient fault experiments slowly developing degradation. Sensor measurements and Condition Monitoring (CM) data extracted from the system allow to follow up the process drift. The prognosis architecture we propose makes use of a dynamical clustering algorithm to model the data in a feature space. This algorithm uses a sliding window scheme on which the model is iteratively updated. Metrics applied on the parameters of this model are used to compute a drift severity indicator, which is also an indicator of the health of the system. The architecture for prognosis is applied on a benchmark of wind turbine. The used benchmark has been constructed to serve as a realistic wind turbine model. It was used in the context of a global scale fault diagnosis and fault tolerant control competition. The benchmark also proposed a drifting fault scenario that we used to test our approach.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094781","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. Vieira, M. A. M. Cabral, T. V. Menezes, B. E. Silva, A. C. Lisboa
While there are many functions defined in the literature to measure the error magnitude (how much), the problem of dinning the spatial error (where) is not so well defined. For instance, in a given region it is expected a global growth in the electrical demand of 10MW. For the electrical system planning not only the amount but also the location must be considered. Predicting a growth of 10MW (how much) in the south (where) of a city would lead to complete different polices in terms of resources allocation (for instance a new substation) than predicting the same amount of 10MW in the north. Trying to cope with this difficulty, this paper proposes the concept of spatial error as the cost of transporting the surplus of one region to compensate another region deceit. This conceptual problem was written as an optimization transportation problem. This paper describes conceptually the difference between magnitude and spatial error measures and shows an algorithm to deal efficiently with the defined framework.
{"title":"Measuring the Spatial Error in Load Forecasting for Electrical Distribution Planning as a Problem of Transporting the Surplus to the In-Deficit Locations","authors":"D. Vieira, M. A. M. Cabral, T. V. Menezes, B. E. Silva, A. C. Lisboa","doi":"10.1109/ICMLA.2012.203","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.203","url":null,"abstract":"While there are many functions defined in the literature to measure the error magnitude (how much), the problem of dinning the spatial error (where) is not so well defined. For instance, in a given region it is expected a global growth in the electrical demand of 10MW. For the electrical system planning not only the amount but also the location must be considered. Predicting a growth of 10MW (how much) in the south (where) of a city would lead to complete different polices in terms of resources allocation (for instance a new substation) than predicting the same amount of 10MW in the north. Trying to cope with this difficulty, this paper proposes the concept of spatial error as the cost of transporting the surplus of one region to compensate another region deceit. This conceptual problem was written as an optimization transportation problem. This paper describes conceptually the difference between magnitude and spatial error measures and shows an algorithm to deal efficiently with the defined framework.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129667613","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}
A. B. Nassif, Luiz Fernando Capretz, D. Ho, Mohammad Azzeh
Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Tree boost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Tree boost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Tree boost model can be used with promising results to estimate software effort.
{"title":"A Treeboost Model for Software Effort Estimation Based on Use Case Points","authors":"A. B. Nassif, Luiz Fernando Capretz, D. Ho, Mohammad Azzeh","doi":"10.1109/ICMLA.2012.155","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.155","url":null,"abstract":"Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Tree boost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Tree boost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Tree boost model can be used with promising results to estimate software effort.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568040","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}
Sriraam Natarajan, Saket Joshi, B. Saha, A. Edwards, Tushar Khot, Elizabeth Moody, K. Kersting, C. Whitlow, J. Maldjian
Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages - (1) a segmentation layer where brain MRI data is divided into clinically relevant regions, (2) a classification layer that uses relational learning algorithms to make pair wise predictions between the three classes, and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain expert's knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimer's Disease Neuroimaging Initiative and demonstrate that it obtains state-of the-art performance with minimal feature engineering.
{"title":"A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain","authors":"Sriraam Natarajan, Saket Joshi, B. Saha, A. Edwards, Tushar Khot, Elizabeth Moody, K. Kersting, C. Whitlow, J. Maldjian","doi":"10.1109/ICMLA.2012.42","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.42","url":null,"abstract":"Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages - (1) a segmentation layer where brain MRI data is divided into clinically relevant regions, (2) a classification layer that uses relational learning algorithms to make pair wise predictions between the three classes, and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain expert's knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimer's Disease Neuroimaging Initiative and demonstrate that it obtains state-of the-art performance with minimal feature engineering.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130768548","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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