R. Karimi, Martin Wistuba, A. Nanopoulos, L. Schmidt-Thieme
A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to use active learning techniques and ask the new user to rate a few items to reveal her preferences. The sequence of queries should not be static, i.e in each step the best query depends on the responses of the new user to the previous queries. Decision trees have been proposed to capture the dynamic aspect of this process. In this paper we improve decision trees in two ways. First, we propose the Most Popular Sampling (MPS) method to increase the speed of the tree construction. In each node, instead of checking all candidate items, only those which are popular among users associated with the node are examined. Second, we develop a new algorithm to build decision trees. It is called Factorized Decision Trees (FDT) and exploits matrix factorization to predict the ratings at nodes of the tree. The experimental results on the Netflix dataset show that both contributions are successful. The MPS increases the speed of the tree construction without harming the accuracy. And FDT improves the accuracy of rating predictions especially in the last queries.
{"title":"Factorized Decision Trees for Active Learning in Recommender Systems","authors":"R. Karimi, Martin Wistuba, A. Nanopoulos, L. Schmidt-Thieme","doi":"10.1109/ICTAI.2013.67","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.67","url":null,"abstract":"A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to use active learning techniques and ask the new user to rate a few items to reveal her preferences. The sequence of queries should not be static, i.e in each step the best query depends on the responses of the new user to the previous queries. Decision trees have been proposed to capture the dynamic aspect of this process. In this paper we improve decision trees in two ways. First, we propose the Most Popular Sampling (MPS) method to increase the speed of the tree construction. In each node, instead of checking all candidate items, only those which are popular among users associated with the node are examined. Second, we develop a new algorithm to build decision trees. It is called Factorized Decision Trees (FDT) and exploits matrix factorization to predict the ratings at nodes of the tree. The experimental results on the Netflix dataset show that both contributions are successful. The MPS increases the speed of the tree construction without harming the accuracy. And FDT improves the accuracy of rating predictions especially in the last queries.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114979547","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}
For many hard combinatorial problems that arise from real-world applications, the conventional theory of algorithms and complexity cannot give reasonable (i.e., polytime) performance guarantees and considers such problems as intractable. Nevertheless, heuristics-based algorithms and solvers work surprisingly well on real-world instances, which suggests that our world may be “friendly enough” to make many typical computational tasks poly-time- challenging the value of the conventional worst-case complexity view in CS (Bart Selman, 2012). Indeed, there is an enormous gap between theoretical performance guarantees and the empirically observed performance of solvers. Efficient solvers exploit the “hidden structure” of real-world problems, and so a theoretical framework that explains practical problem hardness and easiness must not ignore such structural aspects.
{"title":"Capturing structure in hard combinatorial problems","authors":"Stefan Szeider","doi":"10.1109/ICTAI.2013.136","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.136","url":null,"abstract":"For many hard combinatorial problems that arise from real-world applications, the conventional theory of algorithms and complexity cannot give reasonable (i.e., polytime) performance guarantees and considers such problems as intractable. Nevertheless, heuristics-based algorithms and solvers work surprisingly well on real-world instances, which suggests that our world may be “friendly enough” to make many typical computational tasks poly-time- challenging the value of the conventional worst-case complexity view in CS (Bart Selman, 2012). Indeed, there is an enormous gap between theoretical performance guarantees and the empirically observed performance of solvers. Efficient solvers exploit the “hidden structure” of real-world problems, and so a theoretical framework that explains practical problem hardness and easiness must not ignore such structural aspects.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128212739","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}
Music recommendation systems based on Collaborative Filtering methods have been extensively developed over the last years. Typically, they work by analyzing the past user-song relationships, and provide informed guesses based on the overall information collected from other users. Although the music listening behavior is a repetitive and time-dependent process, these methods have not taken this into account and only consider user-song interaction for recommendation. In this work, we explore the usage of temporal context and session diversity in Session-based Collaborative Filtering techniques for music recommendation. We compared two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions, the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns. We evaluated the developed algorithms by measuring the Hit Ratio, and the Mean Reciprocal Rank. Results reveal that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF.
{"title":"Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context","authors":"Ricardo J. Dias, Manuel J. Fonseca","doi":"10.1109/ICTAI.2013.120","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.120","url":null,"abstract":"Music recommendation systems based on Collaborative Filtering methods have been extensively developed over the last years. Typically, they work by analyzing the past user-song relationships, and provide informed guesses based on the overall information collected from other users. Although the music listening behavior is a repetitive and time-dependent process, these methods have not taken this into account and only consider user-song interaction for recommendation. In this work, we explore the usage of temporal context and session diversity in Session-based Collaborative Filtering techniques for music recommendation. We compared two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions, the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns. We evaluated the developed algorithms by measuring the Hit Ratio, and the Mean Reciprocal Rank. Results reveal that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127129544","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}
Cloud computing offers the possibility to build sophisticated software systems on virtualized infrastructures at a fraction of the cost necessary just a few years ago. Nevertheless, the deployment of such complex systems is a serious issue due to the large number of involved software packages and services, and to their elaborated interdependencies. In this paper we address the challenge of automatizing this complex deployment process. We first formalize it as a planning problem and observe that standard planning tools can effectively solve it only on small and trivial instances. For this reason, we propose an ad hoc planning technique which we validate by means of a prototype implementation able to effectively solve this deployment problem also on instances of realistic size.
{"title":"A Planning Tool Supporting the Deployment of Cloud Applications","authors":"Tudor A. Lascu, J. Mauro, G. Zavattaro","doi":"10.1109/ICTAI.2013.41","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.41","url":null,"abstract":"Cloud computing offers the possibility to build sophisticated software systems on virtualized infrastructures at a fraction of the cost necessary just a few years ago. Nevertheless, the deployment of such complex systems is a serious issue due to the large number of involved software packages and services, and to their elaborated interdependencies. In this paper we address the challenge of automatizing this complex deployment process. We first formalize it as a planning problem and observe that standard planning tools can effectively solve it only on small and trivial instances. For this reason, we propose an ad hoc planning technique which we validate by means of a prototype implementation able to effectively solve this deployment problem also on instances of realistic size.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130110093","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}
The shape, or contour, of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be addressed. The first is to obtain clean edges, and the second is to organize those edges into a structured data form upon which the necessary manipulations and analysis may be performed. Simple cells in the primary visual cortex are specialized in orientation detection, so the neural mechanism can be simulated by a computational model, which can produce a fairly clean set of lines, and all of them in vectors rather than in pixels. Then a line-context descriptor was designed to describe geometrical distribution of lines in a local area. All lines were also recorded by a weighted graph, and its minimum spanning tree can be used to describe the topological features of an object. An iterative matching algorithm was developed by combining line-context descriptors and minimum spanning tree, and was shown to match objects of the same type but with different shapes very well. Our results suggest that key to representation efficiency of searchable trees is to apply a mid-level line-context. This once more confirms the crucial role played by simple cells in visual processing path, for its preprocessing can greatly ease the subsequent processing.
{"title":"A Shape Recognition Method Based on Graph- and Line-Contexts","authors":"Hui Wei, Jinwen Xiao","doi":"10.1109/ICTAI.2013.44","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.44","url":null,"abstract":"The shape, or contour, of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be addressed. The first is to obtain clean edges, and the second is to organize those edges into a structured data form upon which the necessary manipulations and analysis may be performed. Simple cells in the primary visual cortex are specialized in orientation detection, so the neural mechanism can be simulated by a computational model, which can produce a fairly clean set of lines, and all of them in vectors rather than in pixels. Then a line-context descriptor was designed to describe geometrical distribution of lines in a local area. All lines were also recorded by a weighted graph, and its minimum spanning tree can be used to describe the topological features of an object. An iterative matching algorithm was developed by combining line-context descriptors and minimum spanning tree, and was shown to match objects of the same type but with different shapes very well. Our results suggest that key to representation efficiency of searchable trees is to apply a mid-level line-context. This once more confirms the crucial role played by simple cells in visual processing path, for its preprocessing can greatly ease the subsequent processing.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130736328","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}
Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of few and noisy training examples. We propose a hybrid neural network plait for improving the classification performance of state-of-the-art learning models applied to the images of such data sets. The improvement is reached by (1) using additionally to the images different further side information delivering different feature sets and requiring different learning models, (2) retraining all different learning models interactively within one common structure. The proposed hybrid neural network plait architecture reached in the experiments with 2 different data sets on average a classification performance improvement of 40% and 52% compared to a single convolutional neural network and 13% and 17% compared to a stacking ensemble method.
{"title":"HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information","authors":"R. Janning, Carlotta Schatten, L. Schmidt-Thieme","doi":"10.1109/ICTAI.2013.15","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.15","url":null,"abstract":"Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of few and noisy training examples. We propose a hybrid neural network plait for improving the classification performance of state-of-the-art learning models applied to the images of such data sets. The improvement is reached by (1) using additionally to the images different further side information delivering different feature sets and requiring different learning models, (2) retraining all different learning models interactively within one common structure. The proposed hybrid neural network plait architecture reached in the experiments with 2 different data sets on average a classification performance improvement of 40% and 52% compared to a single convolutional neural network and 13% and 17% compared to a stacking ensemble method.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132033459","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 many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, class-imbalance and cost-sensitive learning have attracted much attention from researchers. Sampling is one of the widely used approaches in dealing with the class imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we study the effect of sampling the natural training data on state-of-the-art Bayesian network classifiers, such as Naive Bayes (NB), Tree Augmented Naïve Bayes (TAN), Averaged One-Dependence Estimators (AODE), Weighted Average of One-Dependence Estimators (WAODE), and Hidden naive Bayes (HNB) and propose sampled Bayesian network classifiers. Our experimental results on a large number of UCI datasets show that our sampled Bayesian network classifiers perform much better than the ones trained from the natural training data especially when the natural training data is highly imbalanced and the cost ratio is high enough.
{"title":"Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning","authors":"Liangxiao Jiang, Chaoqun Li, Z. Cai, Harry Zhang","doi":"10.1109/ICTAI.2013.82","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.82","url":null,"abstract":"In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, class-imbalance and cost-sensitive learning have attracted much attention from researchers. Sampling is one of the widely used approaches in dealing with the class imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we study the effect of sampling the natural training data on state-of-the-art Bayesian network classifiers, such as Naive Bayes (NB), Tree Augmented Naïve Bayes (TAN), Averaged One-Dependence Estimators (AODE), Weighted Average of One-Dependence Estimators (WAODE), and Hidden naive Bayes (HNB) and propose sampled Bayesian network classifiers. Our experimental results on a large number of UCI datasets show that our sampled Bayesian network classifiers perform much better than the ones trained from the natural training data especially when the natural training data is highly imbalanced and the cost ratio is high enough.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123469422","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}
Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings -- planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.
{"title":"An Automatic Algorithm Selection Approach for Planning","authors":"M. Vallati, L. Chrpa, D. Kitchin","doi":"10.1109/ICTAI.2013.12","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.12","url":null,"abstract":"Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings -- planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"12 1-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080942","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}
Achievement of robotic goals generally needs both plan synthesis and plan execution through physical motions. Costs of actions in robotic tasks are generally motion-dependent. Generally there are many action-based plans for achieving a goal and usually there are many motion plans for executing each action-based plan. Many efficient action-based planners and motion planners have been developed in the last twenty years. One can exploit these computational advances to find low-cost motion plans from the space of motion plans for executing a large number of action-based plans. In this paper we report on generation of action-based plans with low motion-related cost for their execution. We report on empirical evaluation which shows that the motion-related costs for executing action-based plans found with our approach are lower than those for action-based plans found with no motion cost information available to the action-based planner.
{"title":"Motion-Driven Action-Based Planning","authors":"Brandon Ellenberger, A. Mali","doi":"10.1109/ICTAI.2013.128","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.128","url":null,"abstract":"Achievement of robotic goals generally needs both plan synthesis and plan execution through physical motions. Costs of actions in robotic tasks are generally motion-dependent. Generally there are many action-based plans for achieving a goal and usually there are many motion plans for executing each action-based plan. Many efficient action-based planners and motion planners have been developed in the last twenty years. One can exploit these computational advances to find low-cost motion plans from the space of motion plans for executing a large number of action-based plans. In this paper we report on generation of action-based plans with low motion-related cost for their execution. We report on empirical evaluation which shows that the motion-related costs for executing action-based plans found with our approach are lower than those for action-based plans found with no motion cost information available to the action-based planner.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114233364","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}
Chaoqun Li, Liangxiao Jiang, Hongwei Li, Shasha Wang
Classification is an important task in data mining, while accurate class probability estimation is also desirable in real-world applications. Some probability-based classifiers, such as the k-nearest neighbor algorithm (KNN) and its variants, can estimate the class membership probabilities of the test instance. Unfortunately, a good classifier is not always a good class probability estimator. In this paper, we try to improve the class probability estimation performance of KNN and its variants. As we all know, KNN and its variants are all of the distance-related algorithms and their performance is closely related to the used distance metric. Value Difference Metric (VDM) is one of the widely used distance metrics for nominal attributes. Thus, in order to scale up the class probability estimation performance of the distance-related algorithms such as KNN and its variants, we propose an Attribute Weighted Value Difference Metric (AWVDM) in this paper. AWVDM uses the mutual information between the attribute variable and the class variable to weight the difference between two attribute values of each pair of instances. Experimental results on 36 UCI benchmark datasets validate the effectiveness of the proposed AWVDM.
{"title":"Attribute Weighted Value Difference Metric","authors":"Chaoqun Li, Liangxiao Jiang, Hongwei Li, Shasha Wang","doi":"10.1109/ICTAI.2013.91","DOIUrl":"https://doi.org/10.1109/ICTAI.2013.91","url":null,"abstract":"Classification is an important task in data mining, while accurate class probability estimation is also desirable in real-world applications. Some probability-based classifiers, such as the k-nearest neighbor algorithm (KNN) and its variants, can estimate the class membership probabilities of the test instance. Unfortunately, a good classifier is not always a good class probability estimator. In this paper, we try to improve the class probability estimation performance of KNN and its variants. As we all know, KNN and its variants are all of the distance-related algorithms and their performance is closely related to the used distance metric. Value Difference Metric (VDM) is one of the widely used distance metrics for nominal attributes. Thus, in order to scale up the class probability estimation performance of the distance-related algorithms such as KNN and its variants, we propose an Attribute Weighted Value Difference Metric (AWVDM) in this paper. AWVDM uses the mutual information between the attribute variable and the class variable to weight the difference between two attribute values of each pair of instances. Experimental results on 36 UCI benchmark datasets validate the effectiveness of the proposed AWVDM.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121250065","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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