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This paper deals with modeling human behavior routines during driving. We propose a new vision of the maximum causal entropy framework for inverse reinforcement learning to predict actions to be triggered in particular situation (lane change). We designed a plugin to enhance functionalities of the vCar platform which is presents an open source solution for the analysis and visualization of data from...
Decision Tree is one of the most popular supervised Machine Learning algorithms; it is also the easiest to understand. But finding an optimal decision tree for a given data is a harder task and the use of multiple performance metrics adds some complexity to the problem of selecting the most appropriate DT.
Redescription mining aims at finding pairs of queries over data variables that describe roughly the same set of observations. These redescriptions can be used to obtain different views on the same set of entities. So far, redescription mining methods have aimed at listing all redescriptions supported by the data. Such an approach can result in many redundant redescriptions and hinder the user's ability...
To extract knowledge from Data bases Data mining is being used. Data mining is associated with various techniques. In those Clustering is considered to be one of the best approaches. Clustering a huge data set specifically categorical data is difficult and tedious procedure. In this context a proficient method is proposed that is focused on Rough purity for humanizing accuracy of grouping and keeping...
We introduce the notion of Optimal Patterns (OPs), defined as the best patterns according to a given user preference, and show that OPs encompass many data mining problems. Then, we propose a generic method based on a Dynamic Constraint Satisfaction Problem to mine OPs, and we show that any OP is characterized by a basic constraint and a set of constraints to be dynamically added. Finally, we perform...
The discovery of informative itemsets is a fundamental building block in data analytics and information retrieval. While the problem has been widely studied, only few solutions scale. This is particularly the case when i) the data set is massive, calling for large-scale distribution, and/or ii) the length k of the informative itemset to be discovered is high. In this paper, we address the problem...
The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where speed and storage costs can be reduced is via symbolization as a pre-processing step, additionally opening up the use of an array of discrete algorithms...
Symbolic time series analysis (STSA) is built upon the concept of symbolic dynamics that deals with discretization of dynamical systems in both space and time. The notion of STSA has led to the development of a pattern recognition tool in the paradigm of dynamic data-driven application systems (DDDAS), where a time series of sensor signals is partitioned to obtain a symbol sequence that, in turn,...
Clustering is a distribution of data into groups of similar objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The concept of clustering applications is particularly in the context of information retrieval and in organizing web resources. The objective of clustering is to find out information and in...
The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than...
Differential Evolution (DE) is a numerical optimization approach, which is simple to implement, requires little parameter tuning, and known for remarkable performance. It mainly uses the distance and direction information from the current population to guide its further search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool...
The concepts of symbolic dynamics and partitioning of time series data have been used for feature extraction and anomaly detection. Although much attention has been paid to modeling of finite state machines from symbol sequences, similar efforts have not been expended for partitioning of time series data to optimally generate symbol sequences. This paper addresses this issue and proposes a partitioning...
Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses...
The particle swarm optimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarm optimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed...
In pattern recognition system, many irrelevant or redundant features will not only reduce the performance of classifier but also lead to the "dimension disaster", so it is important to select features. This thesis proposes a new method of feature subset selection, which is based on discrete binary version of particle swarm optimization (BPSO) and overlap information entropy (OIE). This method...
Feature selection is a most important procedure which can affect the performance of pattern recognition systems. Since most feature selection algorithms easily fall into local optimum, a novel ant colony optimization approach to feature selection based on fuzzy entropy is proposed (ACOFE). In the proposed algorithm, fuzzy entropy is adopted as pheromone information for ant colony optimization. In...
The segmentation method based on the optimization of only one criterion does not work well for a lot of images; even when equipped with the optimal value of the threshold of its single criterion, the segmentation program does not produce a satisfactory result. In this paper, image segmentation based on multiobjective optimization is presented. It combines 2-D maximum entropy and 2-D Otsu method using...
In this paper, the cross-entropy (CE) method is proposed to solve non-linear discriminant analysis or kernel Fisher discriminant (CE-KFD) analysis. CE through certain steps can find the optimal or near optimal solution with a fast rate of convergence for optimization problem. While, KFD is to solve problem of Fisher's linear discriminant in a kernel feature space F by maximizing between class variance...
This paper presents a new stochastic chance-constrained 0-1 integer programming model for investigating the investment combination problem in multi-project multi-item investment combination. The proposed model includes two objectives with stochastic constraints to construct a 0-1 integer programming model. On the one hand, the risk value will be measured by negative entropy; on the other hand, the...
Conservation of information (COI) popularized by the no free lunch theorem is a great leveler of search algorithms, showing that on average no search outperforms any other. Yet in practice some searches appear to outperform others. In consequence, some have questioned the significance of COI to the performance of search algorithms. An underlying foundation of COI is Bernoulli's Principle of Insufficient...
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