We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

On combining classifiers

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Combining Pattern Classifiers

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

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

Myoelectric control systems—A survey

In the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. The authors propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters confirm the superiority of the presented method to the other voting techniques. >

Combining multiple neural networks by fuzzy integral for robust classification

The sign language is a method of communication for the deaf-mute. Articulated gestures and postures of hands and fingers are commonly used for the sign language. This paper presents a system which recognizes the Korean sign language (KSL) and translates into a normal Korean text. A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition.

A dynamic gesture recognition system for the Korean sign language (KSL)

The objective of this paper is to develop a powered wheelchair controller based on EMG for users with high-level spinal cord injury. EMG is very naturally measured when the user indicating a certain direction and the force information which will be used for the speed of wheelchair is easily extracted from EMG. Furthermore, the emergency situation based on EMG will be checked relatively ease. We classified the pre-defined motions such as rest case, forward movement, left movement, and right movement by fuzzy min-max neural networks (FMMNN). This classification results and evaluation results with real users shows the feasibility of EMG as an input interface for powered wheelchair.

Human-machine interface for wheelchair control with EMG and its evaluation

KARES (KAIST Rehabilitation Engineering System) is a rehabilitation robotic system with 6 degrees of freedom robot arm mounted on the powered wheelchair, in order to assist the disabled and the elderly for independent livelihood. Human machine interaction is very important in this system. The direct control of the robot arm takes a high cognitive load on the user's part while physically disabled persons may have difficulties operating joysticks or push buttons for delicate movements. Therefore, a certain degree of autonomy of the robotic system is needed. To perceive the environment, color vision and force/torque sensors are mounted on the end-effector of the robotic arm of KARES. Four basic tasks are tested: picking up a cup from a table, picking up a pen from the floor, moving an object to the user's face, and operating a switch on a wall. These tasks are performed autonomously in the semi-structured environment.

KARES: intelligent rehabilitation robotic system for the disabled and the elderly

Sign language is a method of communication for deaf persons. In communication using hand gesture, sign words and manual alphabets are used together. In this paper a system is proposed, which recognizes Korean sign Language (KSL). KSL is composed of Korean sign words and Korean manual alphabets continuously. To recognize meanings of continuous gestures which have no token of beginning and end, this system segments current motion states using speed and change of speed in motions and state automata. To understand the meaning of a gesture, basic component classifiers using fuzzy min-max neural network and fuzzy logic are used. Basic elements of meaning used in this system are 14 hand directions, 23 hand postures, and 14 hand orientations. Meaning of signed gesture is interpreted using basic elements which were recognized by 3 classifiers. This system recognizes 31 Korean manual alphabets and 131 Korean signs in real-time with recognition rate 96.7% for Korean manual alphabets and 94.3% for Korean sign words, excluding no recognition case.

Jong-Sung Kim

Papers

Combining multiple neural networks by fuzzy integral for robust classification

A dynamic gesture recognition system for the Korean sign language (KSL)

Human-machine interface for wheelchair control with EMG and its evaluation

KARES: intelligent rehabilitation robotic system for the disabled and the elderly

Real-time recognition system of Korean sign language based on elementary components