This paper describes a hybrid methodology that integrates genetic algorithms (GAs) and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.
Abstract. This paper presents preliminary works on an agent-based approach for distributed learning of decision trees. The distributed decision tree approach is applied to intrusion detection domain, the interest of which is recently increasing. In the approach, a network profile is built by applying a distributed data analysis method for the collection of data from distributed hosts. The method integrates inductive generalization and agent-based computing, so that classification rules are learned via tree induction from distributed data to be used as intrusion profiles. Agents, in a collaborative fashion, generate partial trees and communicate the temporary results among them in the form of indices to the data records. Experimental results are presented for military network domain data used for the network intrusion detection in KDD cup 1999. Several experimental results show that the performance of distributed version of decision tree is much better than that of non-distributed version with data collected manually from distributed hosts.
We address the problem of crafting visual routines f o r detection tasks. Emphasis is placed on both competition and learning to help with specific visual tasks involved in localization and identification. Crafting of visual routines presents difficult optimization problems and leads to evolutionary computation using a hybrid genetic architecture consisting of natural selection, learning, and their beneficial interactions. Base features representations and visual routines f o r detection represented as decision trees are evolved. The visual routine considered is that of eye detection. The experimental results reported herein prove the feasibility of our approach in terms of feature selection ('data compression') and the corresponding eye detection (Pattem recognition').
This paper presents a novel way of combining molphological processing and genetic algorithms (GAS) to generate high-peqormance shape discrimination operators. GAS can evolve operators that discriminate among classes comprising different shapes. The operators are defined as variable structuring elements and can be sequenced as program forms. The population of such operators, evaluated according to an index of peqormance corresponding to shape discrimination ability, evolves into an optimal set of operators using genetic search. Experimental results are presented to illustrate the feasibility of our novel approach for shape discrimination.
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