Peter Andreae

A domain-independentwindow approach to multiclass object detection using genetic programming

This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.

Information transfer and area-time tradeoffs for VLSI multiplication

The need to transfer information between processing elements can be a major factor in determining the performance of a VLSI circuit. We show that communication considerations alone dictate that any VLSI design for computing the 2<italic>n</italic>-bit product of two <italic>n</italic>-bit integers must satisfy the constraint <italic>AT</italic><supscrpt>2</supscrpt> ≥ <italic>n</italic><supscrpt>2</supscrpt>/64 where <italic>A</italic> is the area of the chip and <italic>T</italic> is the time required to perform the computation. This same tradeoff applies to circuits which can shift <italic>n</italic>-bit words through <italic>n</italic> different positions.

A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance. Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically. This paper proposes GPMFC, a multiple-feature construction system for classification problems using genetic programming (GP). This paper takes a nonwrapper approach by introducing a filter-based measure of goodness for constructed features. The constructed, high-level features are functions of original input features. These functions are evolved by GP using an entropy-based fitness function that maximizes the purity of class intervals. A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem. The constructed features are used to transform the original input space to a new space with better separability. Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers. The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers. The constructed features help improve the learning performance of symbolic learners. The constructed features, however, may lack intelligibility.

Improving Web Clustering by Cluster Selection

Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This papers experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.

A Case Study of the Introduction of Computer Science in NZ Schools

For many years computing in New Zealand schools was focused on teaching students how to use computers, and there was little opportunity for students to learn about programming and computer science as formal subjects. In this article we review a series of initiatives that occurred from 2007 to 2009 that led to programming and computer science being made available formally as part of the National Certificate in Educational Achievement (NCEA), the main school-leaving assessment, in 2011. The changes were phased in from 2011 to 2013, and we review this process using the Darmstadt model, including describing the context of the school system, the socio-cultural factors in play before, during and after the changes, the nature of the new standards, the reactions and roles of the various stakeholders, and the teaching materials and methods that developed. The changes occurred very quickly, and we discuss the advantages and disadvantages of having such a rapid process. In all these changes, teachers have emerged as having a central role, as they have been key in instigating and implementing change.

Exploiting underrepresented query aspects for automatic query expansion

Users attempt to express their search goals through web search queries. When a search goal has multiple components or aspects, documents that represent all the aspects are likely to be more relevant than those that only represent some aspects. Current web search engines often produce result sets whose top ranking documents represent only a subset of the query aspects. By expanding the query using the right keywords, the search engine can find documents that represent more query aspects and performance improves. This paper describes AbraQ, an approach for automatically finding the right keywords to expand the query. AbraQ identifies the aspects in the query, identifies which aspects are underrepresented in the result set of the original query, and finally, for any particularly underrepresented aspect, identifies keywords that would enhance that aspects representation and automatically expands the query using the best one. The paper presents experiments that show AbraQ significantly increases the precision of hard queries, whereas traditional automatic query expansion techniques have not improved precision. AbraQ also compared favourably against a range of interactive query expansion techniques that require user involvement including clustering, web-log analysis, relevance feedback, and pseudo relevance feedback.

Query Directed Web Page Clustering

Web page clustering methods categorize and organize search results into semantically meaningful clusters that assist users with search refinement; but finding clusters that are semantically meaningful to users is difficult. In this paper, we describe a new Web page clustering algorithm, QDC, which uses the users query as part of a reliable measure of cluster quality. The new algorithm has five key innovations: a new query directed cluster quality guide that uses the relationship between clusters and the query, an improved cluster merging method that generates semantically coherent clusters by using cluster description similarity in additional to cluster overlap, a new cluster splitting method that fixes the cluster chaining or cluster drifting problem, an improved heuristic for cluster selection that uses the query directed cluster quality guide, and a new method of improving clusters by ranking the pages by relevance to the cluster. We evaluate QDC by comparing its clustering performance against that of four other algorithms on eight data sets (four use full text data and four use snippet data) by using eleven different external evaluation measurements. We also evaluate QDC by informally analysing its real world usability and performance through comparison with six other algorithms on four data sets. QDC provides a substantial performance improvement over other Web page clustering algorithms

Constraint limited generalization: acquiring procedures from examples

Generalization is an essential part of any system that can acquire knowledge from examples. I argue that generalization must be limited by a variety of constraints in order to be useful. This paper gives three principles on how generalization processes should be constrained. It also describes a system for acquiring procedures from examples which is based on these principles and is used to illustrate them.

Learning information extraction patterns from tabular Web pages without manual labelling

We describe a domain independent approach to automatically constructing information extraction patterns for semistructured Web pages. The approach was tested on three corpora containing a series of tabular Web sites from different domains and achieved a success rate of at least 80%. A significant strength of the system is that it can infer extraction patterns from a single training page and does not require any manual labeling of the training page.

Standardized Evaluation Method for Web Clustering Results

Web clustering assists users of a search engine by presenting search results as clusters of related pages. Many clustering algorithms with different characteristics have been developed: but the lack of a standardized Web clustering evaluation method that can evaluate clusterings with different characteristics has prevented effective comparison of algorithms. The paper solves this by introducing a new structure for defining general ideal clusterings and new measurements for evaluating clusterings with different characteristics by comparing them against the general ideal clustering.