16 results on '"Kangavari, Mohammadreza"'
Search Results
2. Solving dimension reduction problems for classification using Promoted Crow Search Algorithm (PCSA)
- Author
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Samieiyan, Behrouz, MohammadiNasab, Poorya, Mollaei, Mostafa Abbas, Hajizadeh, Fahimeh, and Kangavari, Mohammadreza
- Published
- 2022
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3. Novel optimized crow search algorithm for feature selection
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Samieiyan, Behrouz, MohammadiNasab, Poorya, Mollaei, Mostafa Abbas, Hajizadeh, Fahimeh, and Kangavari, Mohammadreza
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- 2022
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- View/download PDF
4. Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model
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Farzanyar, Zahra, Kangavari, Mohammadreza, and Cercone, Nick
- Published
- 2012
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5. Adapted one-versus-all decision trees for data stream classification
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Hashemi, Sattar, Yang, Ying, Mirzamomen, Zahra, and Kangavari, Mohammadreza
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Algorithms -- Analysis ,Data mining -- Evaluation ,Decision tree -- Analysis ,Algorithm ,Data warehousing/data mining ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
One-versus-all (OVA) classifiers learn k individual binary classifiers, each distinguishing the instances of a single class from the instances of all other classes. To classify a new instance, the k classifiers are run, and the one that returns the highest confidence is chosen. Thus, OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each discriminating among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and, hence, high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, traditional OVA does not handle concept change, a key feature of data streams. Second, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Third, OVA's classification accuracy is adversely affected by the imbalanced class distributions in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating, and higher classification accuracy than many existing popular data stream classification algorithms. We expect these results to be of interest to researchers and practitioners because they suggest a simple but very elegant and effective alternative to existing classification schemes for data streams. Index Terms--Mining data streams, one-versus-all classifiers.
- Published
- 2009
6. Cross Split Decision Trees for pattern classification.
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Mirzamomen, Zahra, Fekri, Mohammad Navid, and Kangavari, Mohammadreza
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- 2015
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7. DISTRIBUTED FREQUENT ITEM SETS MINING OVER P2P NETWORKS.
- Author
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FARZANYAR, Zahra and KANGAVARI, Mohammadreza
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DATA mining ,PEER-to-peer architecture (Computer networks) ,ONLINE social networks ,COMPUTER file sharing ,SYNCHRONIZATION - Abstract
Data intensive peer-to-peer (P2P) networks are becoming increasingly popular in applications like social networking, file sharing networks, etc. Data mining in such P2P environments is the new generation of advanced P2P applications. Unfortunately, most of the existing data mining algorithms do not fit well in such environments since they require data that can be accessed in its entirety. It also is not easy due to the requirements of online transactional data streams. In this paper, we have developed a local algorithm for tracing frequent item sets over a P2P network. The performance of the proposed algorithm is comparatively tested and analyzed through a series of experiments. [ABSTRACT FROM AUTHOR]
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- 2015
8. Effect of observer agent on ad hoc teamwork in the pursuit domain.
- Author
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Kheirkhahan, Matin, Kangavari, Mohammadreza, and Farahmandi, Farimah
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- 2013
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9. To Better Handle Concept Change and Noise: A Cellular Automata Approach to Data Stream Classification.
- Author
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Carbonell, Jaime G., Siekmann, Jörg, Orgun, Mehmet A., Thornton, John, Hashemi, Sattar, Yang, Ying, Pourkashani, Majid, and Kangavari, Mohammadreza
- Abstract
A key challenge in data stream classification is to detect changes of the concept underlying the data, and accurately and efficiently adapt classifiers to each concept change. Most existing methods for handling concept changes take a windowing approach, where only recent instances are used to update classifiers while old instances are discarded indiscriminately. However this approach can often be undesirably aggressive because many old instances may not be affected by the concept change and hence can contribute to training the classifier, for instance, reducing the classification variance error caused by insufficient training data. Accordingly this paper proposes a cellular automata (CA) approach that feeds classifiers with most relevant instead of most recent instances. The strength of CA is that it breaks a complicated process down into smaller adaptation tasks, for each a single automaton is responsible. Using neighborhood rules embedded in each automaton and emerging time of instances, this approach assigns a relevance weight to each instance. Instances with high enough weights are selected to update classifiers. Theoretical analyses and experimental results suggest that a good choice of local rules for CA can help considerably speed up updating classifiers corresponding to concept changes, increase classifiers' robustness to noise, and thus offer faster and better classifications for data streams. [ABSTRACT FROM AUTHOR]
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- 2007
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10. Effect of Similar Behaving Attributes in Mining of Fuzzy Association Rules in the Large Databases.
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Gavrilova, Marina, Gervasi, Osvaldo, Kumar, Vipin, Tan, C. J. Kenneth, Taniar, David, Laganà, Antonio, Mun, Youngsong, Choo, Hyunseung, Farzanyar, Zahra, Kangavari, Mohammadreza, and Hashemi, Sattar
- Abstract
Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback. It often produces a huge number of fuzzy associations. This is particularly true for datasets whose attributes are highly correlated. The huge number of fuzzy associations makes it very difficult for a human user to analyze them. Existing research has shown that most of the discovered rules are actually redundant or insignificant. In this paper, we propose a novel technique to overcome this problem.The approach is effective because experiment results show that the set of produced rules is typically very small. Our solution also reduces the size of average transactions and dataset. Our performance study shows that this solution has a superior performance over the other algorithms. Keywords: Data mining, fuzzy association rules, linguistic terms. [ABSTRACT FROM AUTHOR]
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- 2006
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11. EFFICIENT MINING OF FUZZY ASSOCIATION RULES FROM THE PRE-PROCESSED DATASET.
- Author
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Farzanyar, Zahra, Kangavari, Mohammadreza, and Chen, Huajun
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DATA mining ,AUTOMATIC extracting (Information science) ,FUZZY sets ,COMPUTER algorithms ,COMPUTER programming - Abstract
Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. In the end, it produces a huge number of fuzzy associations. This is particularly true for datasets whose attributes are highly correlated. The huge number of fuzzy associations makes it very difficult for a human user to analyze them. Existing research has shown that most of the discovered rules are actually redundant or insignificant. In this paper, we propose a novel technique to overcome these problems; we are preprocessing the data tuples by focusing on similar behaviour attributes and ontology. Finally, the efficiency and advantages of this algorithm have been proved by experimental results. [ABSTRACT FROM AUTHOR]
- Published
- 2012
12. Applying a Fuzzy Association Rule Mining Approach in the Robocup Soccer Simulation Domain.
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Saeed Tabatabaee, Seyyed Mohammad, Rafiee, Ehsan, Abdi, Mohammad Jafar, and Kangavari, Mohammadreza
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ASSOCIATION rule mining ,FUZZY logic ,COMPUTER simulation ,INTELLIGENT agents ,SOFT computing - Abstract
Nowadays multi-agent modeling is one of the important problems in problem-domains in which several agents are involved. One of the main reasons for modeling agents is to become capable of predicting their behaviors in special situations. In this paper a modeling algorithm for multi-agent systems is presented which is applicable in domains like RoboCup coach competitions. (a very chaotic and uncertain domain with large number of agents). This algorithm is a combination of a classic data mining algorithm named association rule mining and fuzzy set theory which leads to a soft computing approach. Also some evaluation methods for recognition of models are presented and finally, some experiments for measuring accuracy of models and evaluation methods are outlined. [ABSTRACT FROM AUTHOR]
- Published
- 2009
13. Improving the Goal-Shooting Skill Using a Genetic-Fuzzy System in the RoboCup Soccer Simulation League.
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Kangavari, Mohammadreza, Abdi, Mohammad Jafar, and Saeed Tabatabaee, Seyyed Mohammad
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FUZZY systems ,GENETIC algorithms ,COMPUTER simulation ,FUZZY logic ,RULE-based programming - Abstract
Most of the problems in the RoboCup soccer domain suffer from the noisy perceptions, noisy actions, and continuous state space. To cope with these problems, using Fuzzy logic can be a proper choice, due to its capabilities of inferring and approximate reasoning under uncertainty. However, designing the entire rule base of a Fuzzy rule base system (FRBS) by an expert is a boring and time consuming task and sometimes the performance of the designed Fuzzy system is far from the optimum, especially in cases that the available knowledge of the system is not enough. In this paper, a rule learning method based on the iterative rule learning (IRL) approach is proposed to generate the entire rule base of an FRBS with the help of genetic algorithms (GAs). The advantage of our proposed method compared to similar approaches in the literature is that our algorithm does not need any training set, which is difficult to collect in many cases; cases like most of the problems existing in the RoboCup soccer domain. As a test case, the goal-shooting problem in the RoboCup 3D soccer simulation league is chosen to be solved using this approach. Simulation tests reveal that with applying the rule learning method proposed in this paper on the goal-shooting problem, not only a rule base with good performance in goal-shooting skill can be obtained, but also the number of rules in the rule base can be decreased by using the general rules in constructing the rule base. [ABSTRACT FROM AUTHOR]
- Published
- 2009
14. Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams.
- Author
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Hashemi, Sattar, Kangavari, Mohammadreza, and Yang, Ying
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FUZZY decision making , *DATA analysis , *DECISION trees , *HOEFFDING'S inequalities , *COMPUTER architecture - Abstract
In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common method is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise and missing values that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are not applicable to data streams. These methods normally need to scan data multiple times whereas learning in data streams can only afford one-pass scan because of data's high speed and huge volume. To solve these problems, this paper proposes a novel classification algorithm, Class Specific Fuzzy Decision Trees (CSFDT), which utilizes fuzzy logic to classify data streams. The base classifier of CSFDT is a binary fuzzy decision tree. Whenever the problem of concern contains q classes (q > 2), CSFDT learns one binary classifier for each class to distinguish instances of this class from instances of the remaining (q - 1) classes. The CSFDT's advantages are three folds. First, it offers an adaptive structure to effectively and efficiently handle concept change. Second, it is robust to noise. Third, it deals with missing values in an elegant way. As a result, accuracy drop can be safely attributed to concept change. Extensive evaluations are conducted to compare CSFDT with representative existing data stream classification algorithms on a large variety of data. Experimental results suggest that CSFDT provides a significant benefit to data stream classification in real-world scenarios where concept changes, noise and missing values coexist. [ABSTRACT FROM AUTHOR]
- Published
- 2008
15. Detecting intrusion transactions in databases using data item dependencies and anomaly analysis.
- Author
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Hashemi, Sattar, Yang, Ying, Zabihzadeh, Davoud, and Kangavari, Mohammadreza
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DATABASES ,COMPUTER access control ,COMPUTER security ,DISTRIBUTED databases ,DATABASE management software ,EXPERT systems ,COMPUTER systems - Abstract
The purpose of the intrusion detection system (IDS) database is to detect transactions that access data without permission. This paper proposes a novel approach to identifying malicious transactions. The approach concentrates on two aspects of database transactions: (1) dependencies among data items and (2) variations of each individual data item which can be considered as time-series data. The advantages are threefold. First, dependency rules among data items are extended to detect transactions that read or write data without permission. Second, a novel behaviour similarity criterion is introduced to reduce the false positive rate of the detection. Third, time-series anomaly analysis is conducted to pinpoint intrusion transactions that update data items with unexpected pattern. As a result, the proposed approach is able to track normal transactions and detect malicious ones more effectively than existing approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
16. A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software.
- Author
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Firouzi F, Rashidi M, Hashemi S, Kangavari M, Bahari A, Daryani NE, Emam MM, Naderi N, Shalmani HM, Farnood A, and Zali M
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- Absorptiometry, Photon, Adolescent, Adult, Aged, Algorithms, Bone Density, Colitis, Ulcerative complications, Colitis, Ulcerative physiopathology, Crohn Disease complications, Crohn Disease physiopathology, Female, Humans, Inflammatory Bowel Diseases physiopathology, Male, Middle Aged, Patient Selection, Risk Factors, Smoking adverse effects, Smoking physiopathology, Software, Decision Trees, Diagnosis, Computer-Assisted methods, Inflammatory Bowel Diseases complications, Osteoporosis diagnosis, Osteoporosis etiology
- Abstract
Background: Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses., Materials and Methods: Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes., Results: Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree., Conclusion: BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources.
- Published
- 2007
- Full Text
- View/download PDF
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