In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a process in automated decision making applications have only recently been discovered by computational intelligence community. Also known under various other names, such as multiple classifier systems, committee of classifiers, or mixture of experts, ensemble based systems have shown to produce favorable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. Design, implementation and application of such systems are the main topics of this article. Specifically, this paper reviews conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined. We discuss popular ensemble based algorithms, such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts; as well as commonly used combination rules, including algebraic combination of outputs, voting based techniques, behavior knowledge space, and decision templates. Finally, we look at current and future research directions for novel applications of ensemble systems. Such applications include incremental learning, data fusion, feature selection, learning with missing features, confidence estimation, and error correcting output codes; all areas in which ensemble systems have shown great promise

Ensemble based systems in decision making

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance Detailed system level architecture and design strategies are presented in this paper Simulation results over several real-world data sets are used to validate the effectiveness of this method

/pdf/incremental-learning-from-stream-data-1dzr83glth.pdf

Incremental Learning From Stream Data

Estimation and decision fusion: A survey

Data fusion has been applied to a large number of fields and the corresponding applications utilize numerous mathematical tools. This survey limits the scope to some aspects of estimation and decision fusion. In estimation fusion our main focus is on the cross-correlation between local estimates from different sources. On the other hand, the problem of decision fusion is discussed with emphasis on the classifier combining techniques.

Estimation and Decision Fusion: A Survey

The purpose of my research was to develop a novel voice control system for the use in the robotized manufacturing cells as well as to create tools providing its simple integration into manufacturing. A comprehensive study of existing problems and their possible solutions has been performed. Unlike some other works, it focused on the specific requirements that should be fulfilled by industrially oriented voice control systems. Analysis of existing solutions related to the natural language processing and those related to various voice control applications has been performed. Its goal was to establish the optimal method of voice command analysis for industrially oriented systems. Finally, a voice control system for manufacturing cells has been developed, implemented and practically verified in the laboratory. Unlike many other solutions, it takes into consideration almost all aspects of voice command processing (speech recognition, syntactic and semantic analysis and spontaneous speech effects) and - most importantly - their mutual influence. To provide the simple system customization (integration into any particular manufacturing cells), a special format for quasi-natural sublanguage syntax definition has been developed. A novel algorithm for semantic analysis, using specific features of voice commands used for controlling industrial devices and machines, has been incorporated into the system. Successful implementation in the educational robotized machining cell shows that industrial applications should be possible in the very next future.

Industrially oriented voice control system

This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm. Experiments of a two-camera unified positioning system are implemented successfully to test the proposed computation structure and algorithms.

Incremental learning with balanced update on receptive fields for multi-sensor data fusion

COM-based software architecture for multisensor fusion system

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Incremental learning with balanced update on receptive fields for multi-sensor data fusion

COM-based software architecture for multisensor fusion system