Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers

An experimental comparison of a genetic algorithm and a hill-climber for term selection

Purpose – The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill-climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, we apply a genetic algorithm (GA) to the same problem. Design/Methodology/Approach – We use a standard TREC test collection from the TREC-8 filtering track, recording mean average precision and recall measures to allow comparison between the hillclimber and GA algorithms. We also vary elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as Elitist and Non-Elitist methods are used, as well as Roulette Wheel and Rank selection GA algorithms. Findings – The results of tests suggest that both techniques are, on average, better than the baseline, but the implemented GA does not match the overall performance of a hill-climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes. Research limitations/implications – The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill-climber. The evidence from term rank order would appear to provide extra useful evidence which hill-climbers can use efficiently and effectively to narrow the search space. Originality/Value – The paper represents the first attempt to compare hill-climbers with GAs on a problem of this type

Personalization of Search Engine Services for Effective Retrieval and Knowledge Management

The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising

A cross-benchmark comparison of 87 learning to rank methods

Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered by the absence of a standard set of evaluation benchmark collections. In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets. Our comparison methodology consists of two components: (1) Normalized Winning Number, which gives insight in the ranking accuracy of the learning to rank method, and (2) Ideal Winning Number, which gives insight in the degree of certainty concerning its ranking accuracy. Evaluation results of 87 learning to rank methods on 20 well-known benchmark datasets are collected through a structured literature search. ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning to rank methods in the Normalized Winning Number and Ideal Winning Number dimensions, listed in increasing order of Normalized Winning Number and decreasing order of Ideal Winning Number