Argument based machine learning

AbstractWe present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus arguments constrain the combinatorial search among possible hypotheses, and also direct the search towards hypotheses that are more comprehensible in the light of expert's background knowledge. In this paper we realize the idea of ABML as rule learning. We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm

Clustering with Argument-Based Machine Learning

The need for improvement of data clustering methods demanded more interactive options with domain experts, which led to the development of algorithms, coined as constrained clustering. These algorithms use domain knowledge in the form of positive must-link and negative cannot-link constraints to improve the quality of detected groups. One of the most overlooked issues in this filed is the effectiveness of constraint elicitation. While the process of constraint elicitation can be a tedious task it can have a significant impact on the quality of clustering. In this thesis we designed and developed a method named Argument-based k-means (AB k-means), which is designed for a more efficient clustering and is based on the paradigm of argument-based machine learning (ABML). The knowledge refinement loop enables the domain expert to articulate his domain knowledge by argumenting automatically chosen problematic cases, while the method with the help of counter examples highlights any shortcomings in the expert’s arguments. We adapted the knowledge refinement loop to the needs of clustering by exposing badly and well clustered cases when eliciting constraints, which are crucial for the improvement of clustering. At the same time the obtained constraints lead to clusters that are consistent with the knowledge of the expert in their chosen domain. For an easier use of the new method we have also developed an interactive application. The effectiveness of our approach was empirically tested on three different experimental domains and compared favourably with an ordinary algorithm for constrained clustering

Hierarchical clustering with argument-based machine learning

Področje odkrivanja skupin (angl.clustering) v podatkih je dandanes dobro raziskano, vendar se še vedno iščejo novi pristopi za izboljšanje kakovosti odkrivanja skupin. Eden izmed takih pristopov je možnost interakcije domenskih strokovnjakov z odkrivanjem skupin tako, da eksperti podajajo domensko znanje v obliki pozitivnih (angl. must-link) in negativnih (angl. cannot-link) omejitev na pare učnih primerov, ki se strokovnjaku zdijo primerni. Tak način podajanja omejitev omogoča izboljšanje odkritih skupin in njihovo večjo skladnost z ekspertnim znanjem. V praksi je podajanje pozitivnih in negativnih omejitev na posamezne pare učnih primerov tipično dolgotrajen in zahteven proces tudi za domenske strokovnjake. V diplomski nalogi se soočimo s problemom zajemanja relevantnega domenskega znanja iz strokovnjaka in v ta namen razvijemo metodo hierarhične-ga razvrščanja v skupine s pomočjo argumentiranega strojnega učenja (angl. Argument-based hierarchical clustering, ABHC). Imenovana metoda temelji na hierarhičnem razvrščanju v skupine in paradigmi argumentiranega strojnega učenja, ki se ukvarja z zajemanjem strokovnjakovega znanja. Metoda avtomatsko izbere učne primere, ki jih smatra kot problematične, in jih predstavi domenskemu strokovnjaku. Ekspertu v dani domeni torej prikaže primere, za katere meni, da ne sodijo v skupino, v katero so bili razvrščeni. Ekspert na problematične primere vnaša domensko znanje v obliki argumentov, metoda pa s pomočjo protiprimerov izpostavlja morebitne pomanjkljivosti ali nekonsistentnosti strokovnjakovih argumentov. Strokovnjak lahko tako dopolni pomanjkljivosti podanih argumentov in s tem dobi bolj kakovostno postavljene omejitve. Le-te pa so ključ za izboljšanje rezultata razvrščanja primerov v skupine. Hkrati so pridobljene omejitve in posledično tudi odkrite skupine skladne s strokovnjakovim znanjem. Razvili smo aplikacijo, ki omogoča interakcijo strokovnjaka s hierarhičnim razvrščanjem v skupine s pomočjo prej omenjene metode. Učinkovitost algoritma smo empirično ovrednotili na treh eksperimentalnih domenah s pomočjo domenskega eksperta in rezultate razvrščanja primerjali s klasičnim algoritmom hierarhičnega razvrščanja v skupine z omejitvami ter dobili spodbudne rezultate. Novo metodo hierarhičnega razvrščanja v skupine smo primerjali tudi s sorodnim algoritmom AB k-means, ki prav tako temelji na odkrivanju skupin s pomočjo argumentiranega strojnega učenja, a se pri tem opira na metodo voditeljev. V izbranih eksperimentalnih domenah smo pokazali, da ABHC občutno izboljša rezultate odkrivanja skupin.Data clustering and data clustering methods are well researched topics nowadays, but there is always room for improvement. One way to improve data clustering methods is to implement them with knowledge from any domain expert. One way to extract knowledge from a given expert is in the form of positive must-link and negative cannot-link pairwise constraints. This type of constraints improves the quality of the detected groups. In real-world applications, extracting knowledge in the form of positive and negative constraints is a challenging and time-consuming task for any expert. In this thesis we address the problem of extracting relevant domain knowledge from any expert and develop a method called Argument-based Hierarchi- cal Clustering (ABHC), which is based on hierarchical clustering and built on the argument-based machine learning paradigm (ABML). The method automatically selects cases that are considered problematic and presents them to the expert. In other words, these problematic cases are cases that are likely to have been clustered into the wrong cluster. The expert then articulates its domain knowledge in the form of arguments and constraints as to why the problematic case should or should not be in the cluster it was clustered into. While the method uses counter examples to expose any shortcomings or inconsistencies in the expert\u27s arguments. The counter examples allow the expert to improve his arguments and as a result we get more e_cient constraints and these are the key to improve the clustering results and not only that, the constraints obtained in this way are more consistent with the knowledge of the expert. We have also developed an interactive application using the aforementioned method to test the e_ectiveness of our approach. The method was tested on three experimental domains using domain expert knowledge. We compared the results with two other algorithms. One is a hierarchical clustering with constraints called Constrained Agglomerative (CA) and the other called Argument-based k-means (AB k-means), which is also based on argumentbased machine learning but uses the k-means algorithm as a clustering method. The results look promising