Sequence Classification Using Statistical Pattern Recognition

Iglesias, José Antonio; Ledezma, Agapito; Sanchís, Araceli

doi:10.1007/978-3-540-74825-0_19

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…According to this aspect and based on the work done in [2], in order to get the most representative set of subsequences from a sequence, we propose the use of a trie data structure [32]. This structure was also used in [33] to classify different sequences and in [34], [35] to classify the behavior patterns of a RoboCup soccer simulation team. The construction of a user profile from a single sequence of commands is done by a three step process:…”

Section: Construction Of the User Behavior Profilementioning

confidence: 99%

Creating Evolving User Behavior Profiles Automatically

Iglesias

Angelov

Ledezma

et al. 2012

IEEE Trans. Knowl. Data Eng.

114

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Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

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Section: Construction Of the User Behavior Profilementioning

confidence: 99%

Creating Evolving User Behavior Profiles Automatically

Iglesias

Angelov

Ledezma

et al. 2012

IEEE Trans. Knowl. Data Eng.

114

View full text Add to dashboard Cite

show abstract

“…This structure was also used in (Iglesias et al 2007) to classify different sequences and in (Kaminka et al 2002;Iglesias et al 2006) to classify the behavior patterns of a RoboCup soccer simulation team. Thus, when a new model needs to be constructed, we create an empty trie, and insert each subsequence of events into it, such that all possible subsequences are accessible and explicitly repre sented.…”

Section: Creating Agent Behavior Model Modulementioning

confidence: 99%

Evolving classification of agents’ behaviors: a general approach

et al. 2010

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By recognizing the behavior of others, many different tasks can be performed, such as to predict their future behavior, to coordinate with them or to assist them. If this behavior recognition can be done automatically, it can be very useful in many applications. However, an agents' behavior is not necessarily fixed but rather it evolves/changes. Thus, it is essential to take into account these changes in any behavior recognition system. In this paper, we present a general approach to the classification of streaming data which represent a specific agent behavior based on evolving systems. The experiment results show that an evolving system based on our approach can effi ciently model and recognize different behaviors in very different domains, in particular, UNIX command line data streams, and intelligent home environments.

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“…According to this aspect, in order to get the most representative set of subsequences from a sequence, we propose the use of a trie data structure [16]. This trie data structure was also used in [17], [18] where a team behavior was learned as well as in [19] to classify the behavior patterns of a RoboCup soccer simulation team.…”

Section: Creating a Computer User Profilementioning

confidence: 99%

User modeling: Through statistical analysis and an evolving classifier

Iglesias

Angelov

Ledezma

et al. 2010

International Conference on Fuzzy Systems

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Abstract-Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a computer user is combined with an evolving method to keep up to date the created profiles. The behavior of a computer is represented in this research as the sequence of commands s/he types during a period of time. This sequence is treated using statistical methods in order to create the corresponding user profile. However, as a user profile is usually not fixed but rather it changes and evolves, we propose a user profile classifier based on Evolving Systems. This paper describes briefly the model creation method and the evolving classifier, which are compared with well established off-line and on-line classifiers.

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Sequence Classification Using Statistical Pattern Recognition

Cited by 13 publications

References 12 publications

Creating Evolving User Behavior Profiles Automatically

Creating Evolving User Behavior Profiles Automatically

Evolving classification of agents’ behaviors: a general approach

User modeling: Through statistical analysis and an evolving classifier

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