Fei Chao

Feature selection Inspired classifier ensemble reduction.

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing systems run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets.

An Infant Development-inspired Approach to Robot Hand-eye Coordination

This paper presents a novel developmental learning approach for hand-eye coordination in an autonomous robotic system. Robotic hand-eye coordination plays an important role in dealing with real-time environments. Under the approach, infant developmental patterns are introduced to build our robots learning system. The method works by first constructing a brain-like computational structure to control the robot, and then by using infant behavioural patterns to build a hand-eye coordination learning algorithm. This work is supported by an experimental evaluation, which shows that the control system is implemented simply, and that the learning approach provides fast and incremental learning of behavioural competence.

Generalized Adaptive Fuzzy Rule Interpolation

As a substantial extension to fuzzy rule interpolation that works based on two neighboring rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system consistency when contradictory results are reached during interpolation. The approach first identifies the exhaustive sets of candidates, with each candidate consisting of a set of interpolation procedures which may jointly be responsible for the system inconsistency. Then, individual candidates are modified such that all contradictions are removed, and thus, interpolation consistency is restored. It has been developed on the assumption that contradictions may only be resulted from the underlying interpolation mechanism, and that all the identified candidates are not distinguishable in terms of their likelihood to be the real culprit. However, this assumption may not hold for real-world situations. This paper, therefore, further develops the adaptive method by taking into account observations, rules, and interpolation procedures, all as diagnosable and modifiable system components. In addition, given the common practice in fuzzy systems that observations and rules are often associated with certainty degrees, the identified candidates are ranked by examining the certainty degrees of its components and their derivatives. From this, the candidate modification is carried out based on such ranking. This study significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes.

A developmental approach to robotic pointing via human-robot interaction

Abstract The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot’s initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human–robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. Developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants.

Robotic Free Writing of Chinese Characters via Human–Robot Interactions

National Natural Science Foundation of China [61203336, 61273338, 61003014]; Major State Basic Research Development Program of China (973 Program) [2013CB329502]; Natural Science Foundation Grants of Fujian Province [2010J01346, 2010J05142]

Improving machine vision via incorporating expectation-maximization into Deep Spatio-Temporal learning

The Deep Spatio-Temporal Inference Network (DeSTIN) is a deep learning architecture which combines un-supervised learning and Bayesian inference. The original version of DeSTIN incorporates k-means clustering inside each processing node. Here we propose to replace k-means with a more sophisticated algorithm, online EM (Expectation Maximization), and show that this improves DeSTINs performance on image classification and restoration tasks.

Robotic Dance in Social Robotics—A Taxonomy

Robotic dance is an important topic in the field of social robotics. Its research has a vital significance to both humans and robotics. This paper presents a review of the state of the art in robotic dance. Robotic dance is classified into four categories: cooperative human-robot dance, imitation of human dance motions, synchronization for music, and creation of robotic choreography. The research methods in each category are discussed. Future research areas are highlighted.

Towards sparse rule base generation for fuzzy rule interpolation

Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases by which the entire input domain is fully covered, whilst fuzzy rule interpolation (FRI) is also able to work with sparse rule bases that may not cover certain observations. Thanks to its ability to work with fewer rules, fuzzy rule interpolation approaches have also been utilised to reduce system complexity by removing those rules which can be approximated by their neighbouring ones for complex fuzzy models. A number of important fuzzy rule base generation approaches have been proposed in the literature, but the majority of these only target dense rule bases for traditional fuzzy inference systems. This paper proposes a novel sparse fuzzy rule base generation method to support FRI. The approach first identifies important rules that cannot be accurately approximated by their neighbouring ones to initialise the rule base. Then the raw rule base is optimised by fine-tuning the membership functions of the fuzzy sets. Experimentation is conducted to demonstrate the working principles of the proposed system, with results comparable to those of traditional methods.

Integration of classifier diversity measures for feature selection-based classifier ensemble reduction

A classifier ensemble combines a set of individual classifier’s predictions to produce more accurate results than that of any single classifier system. However, one classifier ensemble with too many classifiers may consume a large amount of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a novel classifier ensemble reduction framework. The framework converts the ensemble reduction into an optimization problem and uses the harmony search algorithm to find the optimized classifier ensemble. Both pairwise and non-pairwise diversity measure algorithms are applied by the subset evaluation method. For the pairwise diversity measure, three conventional diversity algorithms and one new diversity measure method are used to calculate the diversity’s merits. For the non-pairwise diversity measure, three classical algorithms are used. The proposed subset evaluation methods are demonstrated by the experimental data. In comparison with other classifier ensemble methods, the method implemented by the measurement of the interrater agreement exhibits a high accuracy prediction rate against the current ensembles’ performance. In addition, the framework with the new diversity measure achieves relatively good performance with less computational time.

An NP-complete fragment of fibring logic

The fibring method provides a semantic way to take various modal logics as arguments to produce an integrated one, and the benefit of this method is clear: a stronger expressive power. In this article, we prove the computational complexity of a class of fibring logics. Especially for a fibring logic composed of two S5 systems, we present a novel reduction method, Fibring Structure Mapping, to settle its complexity. Then, we found a special NP -complete fragment for the fibred S5 system. The significance of these results is that, on the one hand, the reduction method presented in this article can be generalized to settle the computational complexity problem of other fibring logics, and on the other hand, they help us to achieve a balance between the expressive power and the difficulty of computation.