Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the cn2, id3 and aq algorithms are compared on three medical classification tasks.

Abstract Despite the fact that many symbolic and neural network (connectionist) learning algorithms address the same problem of learning from classified examples, very little is known regarding their comparative strengths and weaknesses. Experiments comparing the ID3 symbolic learning algorithm with the perception and backpropagation neural learning algorithms have been performed using five large, real-world data sets. Overall, backpropagation performs slightly better than the other two algorithms in terms of classification accuracy on new examples, but takes much longer to train. Experimental results suggest that backpropagation can work significantly better on data sets containing numerical data. Also analyzed empirically are the effects of (1) the amount of training data, (2) imperfect training examples, and (3) the encoding of the desired outputs. Backpropagation occasionally outperforms the other two systems when given relatively small amounts of training data. It is slightly more accurate than ID3 when examples are noisy or incompletely specified. Finally, backpropagation more effectively utilizes a "distributed " output encoding.

Solutions to the problem of learning from examples will have far-reaching benefits, and therefore, the problem is one of the most widely studied in the field of machine learning. The purpose of this study is to investigate a general solution method for the problem, the automatic generation of decision (or classification) trees. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort or faults, where "high" was defined to be in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112,000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort...

This paper examines the induction of classification rules from examples using real-world data. Real-world data is almost always characterized by two features, which are important for the design of an induction algorithm. Firstly, there is often noise present, for example, due to imperfect measuring equipment used to collect the data. Secondly the description language is often incomplete, such that examples with identical descriptions in the language will not always be members of the same class. Many induction systems make the `noiseless domain' assumption that the examples do not contain errors and the description language is complete, and consequently constrain their search for rules to those for which no counterexamples exist in the data used for induction. However, in real-world domains correlations between attributes and classes in a data set are rarely without exceptions. To locate such correlations and induce rules describing them it is also necessary to consider rules which may not classify all the training examples correctly. This paper firstly discusses some of the problems presented by noise and proposes a top-down induction algorithm for induction in real-world domains. Secondly, an experimental comparison of this algorithm with other induction systems is presented using three sets of real-world medical data.

This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess end-game "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to over-challenge stateof -the-art learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the best-performing algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a non-monotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio...

Methods for constructive induction perform automatic transformations of description spaces if representational shortcomings deteriorate the quality of learning. In the context of concept learning and propositional representation languages, feature construction algorithms have been developed in order to improve the accuracy and to decrease the complexity of hypotheses. Particularly, so-called hypothesis-driven constructive induction (HCI) algorithms construct new attributes based upon the analysis of induced hypotheses. A new method for constructive induction, CN2-MCI, is described that applies a single, new constructive operator (o ) in the usual HCI-framework to achieve a more fine-grained analysis of decision rules. o uses a cluster algorithm to map selected features into a new binary feature. Given training examples as input, CN2-MCI computes an inductive hypothesis expressed in terms of the transformed representation. Although this paper presents work in progress, early empirica...

10 The Author 13 Acknowledgements 15 1 Introduction 16 1.1 Definition of Learning : : : : : : : : : : : : : : : : : : : : : : : : 16 1.2 The objectives of ML : : : : : : : : : : : : : : : : : : : : : : : : : 17 1.3 Approaches taken so far : : : : : : : : : : : : : : : : : : : : : : : 18 1.4 Motivation for the project : : : : : : : : : : : : : : : : : : : : : : 20 1.5 The Structure of the Thesis : : : : : : : : : : : : : : : : : : : : : 21 2 Theory of Inductive Learning 22 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 22 2.2 Induction as a Search : : : : : : : : : : : : : : : : : : : : : : : : : 23 2.2.1 The Goal: Hypothesis : : : : : : : : : : : : : : : : : : : : 24 2.2.2 The Search Space: Hypothesis Space : : : : : : : : : : : : 24 2.2.3 The operators : : : : : : : : : : : : : : : : : : : : : : : : : 26 2.3 Approaches : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 27 2.3.1 Statistical Classification : : : : : : : : : : : : : : : : : : : 27...

The behaviour of a learning program as the quantity of data is increased affects to a large extent its applicability on real-world problems. This paper presents the results of a theoretical and experimental investigation of the scalability of four well-known empirical concept-learning programs. In particular it examines the effect of using numeric features in the training set. The theoretical part of the work involved a detailed worstcase computational complexity analysis of the algorithms. The results of the analysis deviate substantially from previously reported estimates, which have mainly examined discrete and finite feature spaces. In order to test these results, a set of experiments was carried out, involving one artificial and two real data sets. The artificial data set introduces a near-worst-case situation for the examined algorithms, while the two real data sets provide an indication of their average-case behaviour. Keywords: empirical concept learning, scalability, decision ...

The field of knowledge engineering has been one of the most visible successes of artificial intelligence (AI) to date. Knowledge acquisition (KA) is the main bottleneck in the knowledge engineer's (KE) work. Machine learning (ML) tools have contributed positively to the process of trying to eliminate or open up this bottleneck. But how do we know whether the field is progressing? How can we establish the progress made in any of its branches? How can we be sure of an advance and take advantage of it? This paper proposes a benchmark as a classifying, comparative and metric criterion for ML tools. The benchmark has been centred on the KE viewpoint, covering some of the characteristics he wants to find in a ML tool. The proposed model has been applied to a set of ML tools, comparing expected and obtained results. Experimentation has validated the model and led to interesting results. Keywords: concept learning, benchmark, measures 2 APPLYING METRICS TO ML TOOLS: A KE APPROACH 1.- INTROD...

Our great researchers John McCarthy, Allen Newell, Claude Shannon and Herb Simon, Ken Thompson and Alan Turing have each put significant effort into computer chess research. It seems that now that computers have reached the grandmaster level and are beginning to vie for the World Championship the AI community should pause to evaluate what significance chess has to the evolving objectives of AI, what contributions have been made to date, and what can be expected in the future. Despite the general interest in chess amongst computer scientists and the significant progress in the last twenty years, there seems to be a lack of appreciation for the field in the AI community. On one hand this is the fruit of success (brute force works, why work on anything else?), but also the result of a focus on performance above all else in the chess community. Also, chess has proved to be too challenging for many of the AI techniques that have been thrown at it. We wish to promote chess as the unique testbed that our founding researchers recognized it to be and increase awareness of its contribution to date.